machine learning research papers 2021

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Machine learning articles from across Nature Portfolio

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

Meta’s AI translation model embraces overlooked languages

More than 7,000 languages are in use throughout the world, but popular translation tools cannot deal with most of them. A translation model that was tested on under-represented languages takes a key step towards a solution.

• David I. Adelani

AI networks reveal how flies find a mate

Artificial neural networks that model the visual system of a male fruit fly can accurately predict the insect’s behaviour in response to seeing a potential mate — paving the way for the building of more complex models of brain circuits.

• Pavan Ramdya

Predicting tumour origin with cytology-based deep learning: hype or hope?

The majority of patients with cancers of unknown primary have unfavourable outcomes when they receive empirical chemotherapy. The shift towards using precision medicine-based treatment strategies involves two options: tissue-agnostic or site-specific approaches. Here, we reflect on how cytology-based deep learning tools can be leveraged in these approaches.

• Nicholas Pavlidis

Latest Research and Reviews

Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users

• Agudemu Borjigin
• Kostas Kokkinakis
• Joshua S. Stohl

Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing

• Hongwei Sun

Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning

A novel deep learning method is developed to analyse Drosophila hearts which is able to predict ageing and study cardiac dysfunction, offering potential for modeling human ailments in Drosophila

• Yash Melkani
• Aniket Pant
• Girish C. Melkani

Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis

• Chunyan Zeng

Estimation of the amount of pear pollen based on flowering stage detection using deep learning

• Takefumi Hiraguri
• Yoshihiro Takemura

Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling

Machine learning can improve scoring methods to evaluate protein–ligand interactions, but achieving good generalization is an outstanding challenge. Cao et al. introduce EquiScore, which is based on a graph neural network that integrates physical knowledge and is shown to have robust capabilities when applied to unseen protein targets.

• Duanhua Cao
• Mingyue Zheng

News and Comment

Meta’s AI system is a boost to endangered languages — as long as humans aren’t forgotten

Automated approaches to translation could provide a lifeline to under-resourced languages, but only if companies engage with the people who speak them.

Need a policy for using ChatGPT in the classroom? Try asking students

Students are the key users of AI chatbots in university settings, but their opinions are rarely solicited when crafting policies. That needs to change, says Maja Zonjić.

• Maja Zonjić

Superfast Microsoft AI is first to predict air pollution for the whole world

The model, called Aurora, also forecasts global weather for ten days — all in less than a minute.

• Carissa Wong

Accelerating AI: the cutting-edge chips powering the computing revolution

Engineers are harnessing the powers of graphics processing units (GPUs) and more, with a bevy of tricks to meet the computational demands of artificial intelligence.

• Dan Garisto

A surprising abundance of pancreatic pre-cancers

AI-based three-dimensional genomic mapping reveals a large abundance of cancer precursors in normal pancreatic tissue — prompting new insights and research directions.

• Karen O’Leary

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Volume 139: International Conference on Machine Learning, 18-24 July 2021, Virtual

Editors: Marina Meila, Tong Zhang

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A New Representation of Successor Features for Transfer across Dissimilar Environments

Majid Abdolshah, Hung Le, Thommen Karimpanal George, Sunil Gupta, Santu Rana, Svetha Venkatesh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1-9

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Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

Kuruge Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Rahimi Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10-20

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Debiasing Model Updates for Improving Personalized Federated Training

Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas, Matthew Mattina, Paul Whatmough, Venkatesh Saligrama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:21-31

Memory Efficient Online Meta Learning

Durmus Alp Emre Acar, Ruizhao Zhu, Venkatesh Saligrama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:32-42

Robust Testing and Estimation under Manipulation Attacks

Jayadev Acharya, Ziteng Sun, Huanyu Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:43-53

GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning

Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:54-65

f-Domain Adversarial Learning: Theory and Algorithms

David Acuna, Guojun Zhang, Marc T. Law, Sanja Fidler ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:66-75

Towards Rigorous Interpretations: a Formalisation of Feature Attribution

Darius Afchar, Vincent Guigue, Romain Hennequin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:76-86

Acceleration via Fractal Learning Rate Schedules

Naman Agarwal, Surbhi Goel, Cyril Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:87-99

A Regret Minimization Approach to Iterative Learning Control

Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:100-109

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:110-119

Label Inference Attacks from Log-loss Scores

Abhinav Aggarwal, Shiva Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:120-129

Deep kernel processes

Laurence Aitchison, Adam Yang, Sebastian W. Ober ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:130-140

How Does Loss Function Affect Generalization Performance of Deep Learning? Application to Human Age Estimation

Ali Akbari, Muhammad Awais, Manijeh Bashar, Josef Kittler ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:141-151

On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting

Shunta Akiyama, Taiji Suzuki ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:152-162

Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks

Maxwell M Aladago, Lorenzo Torresani ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:163-174

A large-scale benchmark for few-shot program induction and synthesis

Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, Joshua Tenenbaum ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:175-186

Robust Pure Exploration in Linear Bandits with Limited Budget

Ayya Alieva, Ashok Cutkosky, Abhimanyu Das ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:187-195

Communication-Efficient Distributed Optimization with Quantized Preconditioners

Foivos Alimisis, Peter Davies, Dan Alistarh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:196-206

Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions

Pierre Alquier ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:207-218

Dataset Dynamics via Gradient Flows in Probability Space

David Alvarez-Melis, Nicolò Fusi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:219-230

Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity

Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Alberto Marchetti-Spaccamela, Rebecca Reiffenhäuser ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:231-242

Safe Reinforcement Learning with Linear Function Approximation

Sanae Amani, Christos Thrampoulidis, Lin Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:243-253

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Automatic variational inference with cascading flows

Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:254-263

Sparse Bayesian Learning via Stepwise Regression

Sebastian E. Ament, Carla P. Gomes ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:264-274

Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards

Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:275-285

Preferential Temporal Difference Learning

Nishanth Anand, Doina Precup ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:286-296

Unitary Branching Programs: Learnability and Lower Bounds

Fidel Ernesto Diaz Andino, Maria Kokkou, Mateus De Oliveira Oliveira, Farhad Vadiee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:297-306

The Logical Options Framework

Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan Decastro, Micah Fry, Daniela Rus ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:307-317

Annealed Flow Transport Monte Carlo

Michael Arbel, Alex Matthews, Arnaud Doucet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:318-330

Permutation Weighting

David Arbour, Drew Dimmery, Arjun Sondhi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:331-341

Analyzing the tree-layer structure of Deep Forests

Ludovic Arnould, Claire Boyer, Erwan Scornet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:342-350

Dropout: Explicit Forms and Capacity Control

Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:351-361

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients

Artem Artemev, David R. Burt, Mark van der Wilk ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:362-372

Deciding What to Learn: A Rate-Distortion Approach

Dilip Arumugam, Benjamin Van Roy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:373-382

Private Adaptive Gradient Methods for Convex Optimization

Hilal Asi, John Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:383-392

Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry

Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:393-403

Combinatorial Blocking Bandits with Stochastic Delays

Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, Sanjay Shakkottai ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:404-413

Dichotomous Optimistic Search to Quantify Human Perception

Julien Audiffren ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:414-424

Federated Learning under Arbitrary Communication Patterns

Dmitrii Avdiukhin, Shiva Kasiviswanathan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:425-435

Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge

Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:436-445

Decomposable Submodular Function Minimization via Maximum Flow

Kyriakos Axiotis, Adam Karczmarz, Anish Mukherjee, Piotr Sankowski, Adrian Vladu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:446-456

Differentially Private Query Release Through Adaptive Projection

Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:457-467

On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent

Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:468-477

On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification

Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, Radu Grosu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:478-489

Uniform Convergence, Adversarial Spheres and a Simple Remedy

Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:490-499

Faster Kernel Matrix Algebra via Density Estimation

Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:500-510

Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees

Kishan Panaganti Badrinath, Dileep Kalathil ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:511-520

Skill Discovery for Exploration and Planning using Deep Skill Graphs

Akhil Bagaria, Jason K Senthil, George Konidaris ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:521-531

Locally Adaptive Label Smoothing Improves Predictive Churn

Dara Bahri, Heinrich Jiang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:532-542

How Important is the Train-Validation Split in Meta-Learning?

Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason Lee, Sham Kakade, Huan Wang, Caiming Xiong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:543-553

Stabilizing Equilibrium Models by Jacobian Regularization

Shaojie Bai, Vladlen Koltun, Zico Kolter ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:554-565

Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification

Yu Bai, Song Mei, Huan Wang, Caiming Xiong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:566-576

Principled Exploration via Optimistic Bootstrapping and Backward Induction

Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:577-587

GLSearch: Maximum Common Subgraph Detection via Learning to Search

Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:588-598

Breaking the Limits of Message Passing Graph Neural Networks

Muhammet Balcilar, Pierre Heroux, Benoit Gauzere, Pascal Vasseur, Sebastien Adam, Paul Honeine ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:599-608

Instance Specific Approximations for Submodular Maximization

Eric Balkanski, Sharon Qian, Yaron Singer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:609-618

Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment

Philip J Ball, Cong Lu, Jack Parker-Holder, Stephen Roberts ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:619-629

Regularized Online Allocation Problems: Fairness and Beyond

Santiago Balseiro, Haihao Lu, Vahab Mirrokni ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:630-639

Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers

Yujia Bao, Shiyu Chang, Regina Barzilay ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:640-650

Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:651-661

Compositional Video Synthesis with Action Graphs

Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:662-673

Approximating a Distribution Using Weight Queries

Nadav Barak, Sivan Sabato ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:674-683

Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization

Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:684-693

Training Quantized Neural Networks to Global Optimality via Semidefinite Programming

Burak Bartan, Mert Pilanci ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:694-704

Beyond $log^2(T)$ regret for decentralized bandits in matching markets

Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:705-715

Optimal Thompson Sampling strategies for support-aware CVaR bandits

Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric Maillard ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:716-726

On Limited-Memory Subsampling Strategies for Bandits

Dorian Baudry, Yoan Russac, Olivier Cappé ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:727-737

Generalized Doubly Reparameterized Gradient Estimators

Matthias Bauer, Andriy Mnih ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:738-747

Directional Graph Networks

Dominique Beaini, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, Pietro Lió ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:748-758

Policy Analysis using Synthetic Controls in Continuous-Time

Alexis Bellot, Mihaela van der Schaar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:759-768

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling

Gregory Benton, Wesley Maddox, Sanae Lotfi, Andrew Gordon Gordon Wilson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:769-779

TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer

Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:780-791

Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis

Jeroen Berrevoets, Ahmed Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:792-802

Learning from Biased Data: A Semi-Parametric Approach

Patrice Bertail, Stephan Clémençon, Yannick Guyonvarch, Nathan Noiry ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:803-812

Is Space-Time Attention All You Need for Video Understanding?

Gedas Bertasius, Heng Wang, Lorenzo Torresani ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:813-824

Confidence Scores Make Instance-dependent Label-noise Learning Possible

Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:825-836

Size-Invariant Graph Representations for Graph Classification Extrapolations

Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:837-851

Principal Bit Analysis: Autoencoding with Schur-Concave Loss

Sourbh Bhadane, Aaron B Wagner, Jayadev Acharya ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:852-862

Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries

Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:863-873

Additive Error Guarantees for Weighted Low Rank Approximation

Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:874-883

Sample Complexity of Robust Linear Classification on Separated Data

Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:884-893

Finding k in Latent $k-$ polytope

Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:894-903

Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction

Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:904-913

TempoRL: Learning When to Act

André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:914-924

Follow-the-Regularized-Leader Routes to Chaos in Routing Games

Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michał Misiurewicz, Georgios Piliouras ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:925-935

Neural Symbolic Regression that scales

Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:936-945

Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

Max Biggs, Wei Sun, Markus Ettl ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:946-956

Scalable Normalizing Flows for Permutation Invariant Densities

Marin Biloš, Stephan Günnemann ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:957-967

Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games

Ilai Bistritz, Nicholas Bambos ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:968-979

Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision

Johan Björck, Xiangyu Chen, Christopher De Sa, Carla P Gomes, Kilian Weinberger ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:980-991

Multiplying Matrices Without Multiplying

Davis Blalock, John Guttag ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:992-1004

One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning

Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1005-1014

Black-box density function estimation using recursive partitioning

Erik Bodin, Zhenwen Dai, Neill Campbell, Carl Henrik Ek ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1015-1025

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lió, Michael Bronstein ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1026-1037

The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning

Roberto Bondesan, Max Welling ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1038-1048

Offline Contextual Bandits with Overparameterized Models

David Brandfonbrener, William Whitney, Rajesh Ranganath, Joan Bruna ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1049-1058

High-Performance Large-Scale Image Recognition Without Normalization

Andy Brock, Soham De, Samuel L Smith, Karen Simonyan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1059-1071

Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo

James Brofos, Roy R Lederman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1072-1081

Reinforcement Learning of Implicit and Explicit Control Flow Instructions

Ethan Brooks, Janarthanan Rajendran, Richard L Lewis, Satinder Singh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1082-1091

Machine Unlearning for Random Forests

Jonathan Brophy, Daniel Lowd ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1092-1104

Value Alignment Verification

Daniel S Brown, Jordan Schneider, Anca Dragan, Scott Niekum ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1105-1115

Model-Free and Model-Based Policy Evaluation when Causality is Uncertain

David A Bruns-Smith ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1116-1126

Narrow Margins: Classification, Margins and Fat Tails

Francois Buet-Golfouse ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1127-1135

Differentially Private Correlation Clustering

Mark Bun, Marek Elias, Janardhan Kulkarni ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1136-1146

Disambiguation of Weak Supervision leading to Exponential Convergence rates

Vivien A Cabannnes, Francis Bach, Alessandro Rudi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1147-1157

Finite mixture models do not reliably learn the number of components

Diana Cai, Trevor Campbell, Tamara Broderick ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1158-1169

A Theory of Label Propagation for Subpopulation Shift

Tianle Cai, Ruiqi Gao, Jason Lee, Qi Lei ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1170-1182

Lenient Regret and Good-Action Identification in Gaussian Process Bandits

Xu Cai, Selwyn Gomes, Jonathan Scarlett ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1183-1192

A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization

Hanqin Cai, Yuchen Lou, Daniel Mckenzie, Wotao Yin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1193-1203

GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training

Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1204-1215

On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization

Xu Cai, Jonathan Scarlett ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1216-1226

High-dimensional Experimental Design and Kernel Bandits

Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1227-1237

A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization

Andrew Campbell, Wenlong Chen, Vincent Stimper, Jose Miguel Hernandez-Lobato, Yichuan Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1238-1248

Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections

Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris Holmes, Mert Gurbuzbalaban, Umut Simsekli ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1249-1260

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1261-1271

Learning from Similarity-Confidence Data

Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1272-1282

Parameter-free Locally Accelerated Conditional Gradients

Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1283-1293

Optimizing persistent homology based functions

Mathieu Carriere, Frederic Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1294-1303

Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt$T Regret

Asaf B Cassel, Tomer Koren ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1304-1313

Multi-Receiver Online Bayesian Persuasion

Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1314-1323

Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data

Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Libi Weiss Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1324-1335

Disentangling syntax and semantics in the brain with deep networks

Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1336-1348

Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees

L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1349-1361

Best Model Identification: A Rested Bandit Formulation

Leonardo Cella, Massimiliano Pontil, Claudio Gentile ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1362-1372

Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research

Johan Samir Obando Ceron, Pablo Samuel Castro ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1373-1383

Learning Routines for Effective Off-Policy Reinforcement Learning

Edoardo Cetin, Oya Celiktutan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1384-1394

Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks

Ciwan Ceylan, Salla Franzén, Florian T. Pokorny ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1395-1406

GRAND: Graph Neural Diffusion

Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, Emanuele Rossi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1407-1418

HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections

Ines Chami, Albert Gu, Dat P Nguyen, Christopher Re ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1419-1429

Goal-Conditioned Reinforcement Learning with Imagined Subgoals

Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1430-1440

Locally Private k-Means in One Round

Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1441-1451

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Michael Chang, Sid Kaushik, Sergey Levine, Tom Griffiths ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1452-1462

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Animashree Anandkumar, Sanja Fidler, Jose M Alvarez ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1463-1472

DeepWalking Backwards: From Embeddings Back to Graphs

Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1473-1483

Differentiable Spatial Planning using Transformers

Devendra Singh Chaplot, Deepak Pathak, Jitendra Malik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1484-1495

Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning

Henry J Charlesworth, Giovanni Montana ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1496-1506

Classification with Rejection Based on Cost-sensitive Classification

Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1507-1517

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills

Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan C Julian, Chelsea Finn, Sergey Levine ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1518-1528

Unified Robust Semi-Supervised Variational Autoencoder

Xu Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1529-1538

Unsupervised Learning of Visual 3D Keypoints for Control

Boyuan Chen, Pieter Abbeel, Deepak Pathak ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1539-1549

Integer Programming for Causal Structure Learning in the Presence of Latent Variables

Rui Chen, Sanjeeb Dash, Tian Gao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1550-1560

Improved Corruption Robust Algorithms for Episodic Reinforcement Learning

Yifang Chen, Simon Du, Kevin Jamieson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1561-1570

Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks

Jiaojiao Fan, Amirhossein Taghvaei, Yongxin Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1571-1581

Neural Feature Matching in Implicit 3D Representations

Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1582-1593

Decentralized Riemannian Gradient Descent on the Stiefel Manifold

Shixiang Chen, Alfredo Garcia, Mingyi Hong, Shahin Shahrampour ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1594-1605

Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation

Chao Chen, Haoyu Geng, Nianzu Yang, Junchi Yan, Daiyue Xue, Jianping Yu, Xiaokang Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1606-1616

Mandoline: Model Evaluation under Distribution Shift

Mayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian, Christopher Re ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1617-1629

Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation

Xiaohui Chen, Xu Han, Jiajing Hu, Francisco Ruiz, Liping Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1630-1639

CARTL: Cooperative Adversarially-Robust Transfer Learning

Dian Chen, Hongxin Hu, Qian Wang, Li Yinli, Cong Wang, Chao Shen, Qi Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1640-1650

Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case

Liyu Chen, Haipeng Luo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1651-1660

SpreadsheetCoder: Formula Prediction from Semi-structured Context

Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1661-1672

Large-Margin Contrastive Learning with Distance Polarization Regularizer

Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1673-1683

Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting

Yuzhou Chen, Ignacio Segovia, Yulia R. Gel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1684-1694

A Unified Lottery Ticket Hypothesis for Graph Neural Networks

Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1695-1706

Network Inference and Influence Maximization from Samples

Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1707-1716

Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps

Renyi Chen, Molei Tao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1717-1727

Analysis of stochastic Lanczos quadrature for spectrum approximation

Tyler Chen, Thomas Trogdon, Shashanka Ubaru ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1728-1739

Large-Scale Multi-Agent Deep FBSDEs

Tianrong Chen, Ziyi O Wang, Ioannis Exarchos, Evangelos Theodorou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1740-1748

Representation Subspace Distance for Domain Adaptation Regression

Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1749-1759

Overcoming Catastrophic Forgetting by Bayesian Generative Regularization

Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1760-1770

Cyclically Equivariant Neural Decoders for Cyclic Codes

Xiangyu Chen, Min Ye ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1771-1780

A Receptor Skeleton for Capsule Neural Networks

Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z Chen, Jian Wu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1781-1790

Accelerating Gossip SGD with Periodic Global Averaging

Yiming Chen, Kun Yuan, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1791-1802

ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael Mahoney, Joseph Gonzalez ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1803-1813

SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation

Wuxinlin Cheng, Chenhui Deng, Zhiqiang Zhao, Yaohui Cai, Zhiru Zhang, Zhuo Feng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1814-1824

Self-supervised and Supervised Joint Training for Resource-rich Machine Translation

Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1825-1835

Exact Optimization of Conformal Predictors via Incremental and Decremental Learning

Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1836-1845

Problem Dependent View on Structured Thresholding Bandit Problems

James Cheshire, Pierre Menard, Alexandra Carpentier ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1846-1854

Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence

Yun Kuen Cheung, Georgios Piliouras ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1855-1865

Understanding and Mitigating Accuracy Disparity in Regression

Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1866-1876

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates

Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1877-1887

Flavio Chierichetti, Ravi Kumar, Andrew Tomkins ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1888-1897

Parallelizing Legendre Memory Unit Training

Narsimha Reddy Chilkuri, Chris Eliasmith ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1898-1907

Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies

Uthsav Chitra, Kimberly Ding, Jasper C.H. Lee, Benjamin J Raphael ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1908-1919

Robust Learning-Augmented Caching: An Experimental Study

Jakub Chłędowski, Adam Polak, Bartosz Szabucki, Konrad Tomasz Żołna ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1920-1930

Unifying Vision-and-Language Tasks via Text Generation

Jaemin Cho, Jie Lei, Hao Tan, Mohit Bansal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1931-1942

Learning from Nested Data with Ornstein Auto-Encoders

Youngwon Choi, Sungdong Lee, Joong-Ho Won ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1943-1952

Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning

Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1953-1963

Label-Only Membership Inference Attacks

Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, Nicolas Papernot ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1964-1974

Modeling Hierarchical Structures with Continuous Recursive Neural Networks

Jishnu Ray Chowdhury, Cornelia Caragea ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1975-1988

Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

Filippos Christianos, Georgios Papoudakis, Muhammad A Rahman, Stefano V Albrecht ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1989-1998

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization

Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:1999-2009

First-Order Methods for Wasserstein Distributionally Robust MDP

Julien Grand Clement, Christian Kroer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2010-2019

Phasic Policy Gradient

Karl W Cobbe, Jacob Hilton, Oleg Klimov, John Schulman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2020-2027

Riemannian Convex Potential Maps

Samuel Cohen, Brandon Amos, Yaron Lipman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2028-2038

Scaling Properties of Deep Residual Networks

Alain-Sam Cohen, Rama Cont, Alain Rossier, Renyuan Xu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2039-2048

Differentially-Private Clustering of Easy Instances

Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2049-2059

Improving Ultrametrics Embeddings Through Coresets

Vincent Cohen-Addad, Rémi De Joannis De Verclos, Guillaume Lagarde ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2060-2068

Correlation Clustering in Constant Many Parallel Rounds

Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2069-2078

Concentric mixtures of Mallows models for top-$k$ rankings: sampling and identifiability

Fabien Collas, Ekhine Irurozki ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2079-2088

Exploiting Shared Representations for Personalized Federated Learning

Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2089-2099

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2100-2111

Fairness and Bias in Online Selection

Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2112-2121

Relative Deviation Margin Bounds

Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2122-2131

A Discriminative Technique for Multiple-Source Adaptation

Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2132-2143

Characterizing Fairness Over the Set of Good Models Under Selective Labels

Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2144-2155

Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering

Romain Couillet, Florent Chatelain, Nicolas Le Bihan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2156-2165

Explaining Time Series Predictions with Dynamic Masks

Jonathan Crabbé, Mihaela Van Der Schaar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2166-2177

Generalised Lipschitz Regularisation Equals Distributional Robustness

Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2178-2188

Environment Inference for Invariant Learning

Elliot Creager, Joern-Henrik Jacobsen, Richard Zemel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2189-2200

Mind the Box: $l_1$-APGD for Sparse Adversarial Attacks on Image Classifiers

Francesco Croce, Matthias Hein ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2201-2211

Parameterless Transductive Feature Re-representation for Few-Shot Learning

Wentao Cui, Yuhong Guo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2212-2221

Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint

Shuang Cui, Kai Han, Tianshuai Zhu, Jing Tang, Benwei Wu, He Huang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2222-2232

GBHT: Gradient Boosting Histogram Transform for Density Estimation

Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2233-2243

ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F P O’Boyle, Hugh Leather ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2244-2253

Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning

Sebastian Curi, Ilija Bogunovic, Andreas Krause ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2254-2264

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability

Mihaela Curmei, Sarah Dean, Benjamin Recht ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2265-2275

Dynamic Balancing for Model Selection in Bandits and RL

Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2276-2285

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

Stéphane D’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, Levent Sagun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2286-2296

Consistent regression when oblivious outliers overwhelm

Tommaso D’Orsi, Gleb Novikov, David Steurer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2297-2306

Offline Reinforcement Learning with Pseudometric Learning

Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2307-2318

A Tale of Two Efficient and Informative Negative Sampling Distributions

Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2319-2329

SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels

Kunal Dahiya, Ananye Agarwal, Deepak Saini, Gururaj K, Jian Jiao, Amit Singh, Sumeet Agarwal, Purushottam Kar, Manik Varma ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2330-2340

Fixed-Parameter and Approximation Algorithms for PCA with Outliers

Yogesh Dahiya, Fedor Fomin, Fahad Panolan, Kirill Simonov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2341-2351

Sliced Iterative Normalizing Flows

Biwei Dai, Uros Seljak ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2352-2364

Convex Regularization in Monte-Carlo Tree Search

Tuan Q Dam, Carlo D’Eramo, Jan Peters, Joni Pajarinen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2365-2375

Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation

Christopher R. Dance, Julien Perez, Théo Cachet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2376-2387

Re-understanding Finite-State Representations of Recurrent Policy Networks

Mohamad H Danesh, Anurag Koul, Alan Fern, Saeed Khorram ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2388-2397

Newton Method over Networks is Fast up to the Statistical Precision

Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechensky, Alexander Gasnikov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2398-2409

BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders

Dominic Danks, Christopher Yau ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2410-2420

Intermediate Layer Optimization for Inverse Problems using Deep Generative Models

Giannis Daras, Joseph Dean, Ajil Jalal, Alex Dimakis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2421-2432

Measuring Robustness in Deep Learning Based Compressive Sensing

Mohammad Zalbagi Darestani, Akshay S Chaudhari, Reinhard Heckel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2433-2444

SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning

Lokesh Chandra Das, Myounggyu Won ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2445-2455

Lipschitz normalization for self-attention layers with application to graph neural networks

George Dasoulas, Kevin Scaman, Aladin Virmaux ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2456-2466

Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers

Jyotikrishna Dass, Rabi Mahapatra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2467-2477

Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data

Deepesh Data, Suhas Diggavi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2478-2488

Catformer: Designing Stable Transformers via Sensitivity Analysis

Jared Q Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re, Chelsea Finn, Percy Liang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2489-2499

Diffusion Source Identification on Networks with Statistical Confidence

Quinlan E Dawkins, Tianxi Li, Haifeng Xu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2500-2509

Bayesian Deep Learning via Subnetwork Inference

Erik Daxberger, Eric Nalisnick, James U Allingham, Javier Antoran, Jose Miguel Hernandez-Lobato ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2510-2521

Adversarial Robustness Guarantees for Random Deep Neural Networks

Giacomo De Palma, Bobak Kiani, Seth Lloyd ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2522-2534

High-Dimensional Gaussian Process Inference with Derivatives

Filip de Roos, Alexandra Gessner, Philipp Hennig ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2535-2545

Transfer-Based Semantic Anomaly Detection

Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2546-2558

Grid-Functioned Neural Networks

Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2559-2567

Multidimensional Scaling: Approximation and Complexity

Erik Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2568-2578

What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

Weijian Deng, Stephen Gould, Liang Zheng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2579-2589

Toward Better Generalization Bounds with Locally Elastic Stability

Zhun Deng, Hangfeng He, Weijie Su ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2590-2600

Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing

Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2601-2610

Heterogeneity for the Win: One-Shot Federated Clustering

Don Kurian Dennis, Tian Li, Virginia Smith ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2611-2620

Kernel Continual Learning

Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, Cees Snoek ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2621-2631

Bayesian Optimization over Hybrid Spaces

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2632-2643

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2644-2653

Versatile Verification of Tree Ensembles

Laurens Devos, Wannes Meert, Jesse Davis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2654-2664

On the Inherent Regularization Effects of Noise Injection During Training

Oussama Dhifallah, Yue Lu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2665-2675

Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time

Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2676-2686

Learning Online Algorithms with Distributional Advice

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2687-2696

A Wasserstein Minimax Framework for Mixed Linear Regression

Theo Diamandis, Yonina Eldar, Alireza Fallah, Farzan Farnia, Asuman Ozdaglar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2697-2706

Context-Aware Online Collective Inference for Templated Graphical Models

Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2707-2716

ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables

Aleksandar Dimitriev, Mingyuan Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2717-2727

XOR-CD: Linearly Convergent Constrained Structure Generation

Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2728-2738

Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach

Tianyu Ding, Zhihui Zhu, Rene Vidal, Daniel P Robinson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2739-2748

Coded-InvNet for Resilient Prediction Serving Systems

Tuan Dinh, Kangwook Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2749-2759

Estimation and Quantization of Expected Persistence Diagrams

Vincent Divol, Theo Lacombe ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2760-2770

On Energy-Based Models with Overparametrized Shallow Neural Networks

Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2771-2782

Kernel-Based Reinforcement Learning: A Finite-Time Analysis

Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2783-2792

Attention is not all you need: pure attention loses rank doubly exponentially with depth

Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2793-2803

How rotational invariance of common kernels prevents generalization in high dimensions

Konstantin Donhauser, Mingqi Wu, Fanny Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2804-2814

Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction

Radu Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2815-2825

Bilinear Classes: A Structural Framework for Provable Generalization in RL

Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2826-2836

Improved Contrastive Divergence Training of Energy-Based Models

Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2837-2848

Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation

Cunxiao Du, Zhaopeng Tu, Jing Jiang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2849-2859

Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation

Elbert Du, Franklyn Wang, Michael Mitzenmacher ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2860-2869

Estimating $α$-Rank from A Few Entries with Low Rank Matrix Completion

Yali Du, Xue Yan, Xu Chen, Jun Wang, Haifeng Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2870-2879

Learning Diverse-Structured Networks for Adversarial Robustness

Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2880-2891

Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning

Yaqi Duan, Chi Jin, Zhiyuan Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2892-2902

Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network

Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren, Mingyuan Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2903-2913

Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics

Arkopal Dutt, Andrey Lokhov, Marc D Vuffray, Sidhant Misra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2914-2925

Reinforcement Learning Under Moral Uncertainty

Adrien Ecoffet, Joel Lehman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2926-2936

Confidence-Budget Matching for Sequential Budgeted Learning

Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2937-2947

Self-Paced Context Evaluation for Contextual Reinforcement Learning

Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2948-2958

Provably Strict Generalisation Benefit for Equivariant Models

Bryn Elesedy, Sheheryar Zaidi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2959-2969

Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations

Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2970-2981

Implicit Bias of Linear RNNs

Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K Fletcher ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2982-2992

Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs

Tolga Ergen, Mert Pilanci ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:2993-3003

Revealing the Structure of Deep Neural Networks via Convex Duality

Tolga Ergen, Mert Pilanci ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3004-3014

Whitening for Self-Supervised Representation Learning

Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3015-3024

Graph Mixture Density Networks

Federico Errica, Davide Bacciu, Alessio Micheli ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3025-3035

Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data

Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3036-3046

Weight-covariance alignment for adversarially robust neural networks

Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3047-3056

Data augmentation for deep learning based accelerated MRI reconstruction with limited data

Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3057-3067

Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks

Xuhui Fan, Bin Li, Yaqiong Li, Scott A. Sisson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3068-3077

Model-based Reinforcement Learning for Continuous Control with Posterior Sampling

Ying Fan, Yifei Ming ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3078-3087

SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Animashree Anandkumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3088-3099

On Estimation in Latent Variable Models

Guanhua Fang, Ping Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3100-3110

On Variational Inference in Biclustering Models

Guanhua Fang, Ping Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3111-3121

Learning Bounds for Open-Set Learning

Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3122-3132

Streaming Bayesian Deep Tensor Factorization

Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3133-3142

PID Accelerated Value Iteration Algorithm

Amir-Massoud Farahmand, Mohammad Ghavamzadeh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3143-3153

Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise

Vivek Farias, Andrew A Li, Tianyi Peng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3154-3163

Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results

Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3164-3173

Train simultaneously, generalize better: Stability of gradient-based minimax learners

Farzan Farnia, Asuman Ozdaglar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3174-3185

Unbalanced minibatch Optimal Transport; applications to Domain Adaptation

Kilian Fatras, Thibault Sejourne, Rémi Flamary, Nicolas Courty ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3186-3197

Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach

Yingjie Fei, Zhuoran Yang, Zhaoran Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3198-3207

Lossless Compression of Efficient Private Local Randomizers

Vitaly Feldman, Kunal Talwar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3208-3219

Dimensionality Reduction for the Sum-of-Distances Metric

Zhili Feng, Praneeth Kacham, David Woodruff ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3220-3229

Reserve Price Optimization for First Price Auctions in Display Advertising

Zhe Feng, Sebastien Lahaie, Jon Schneider, Jinchao Ye ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3230-3239

Uncertainty Principles of Encoding GANs

Ruili Feng, Zhouchen Lin, Jiapeng Zhu, Deli Zhao, Jingren Zhou, Zheng-Jun Zha ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3240-3251

Pointwise Binary Classification with Pairwise Confidence Comparisons

Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3252-3262

Provably Correct Optimization and Exploration with Non-linear Policies

Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3263-3273

KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation

Haozhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu, Fei Wu, Chao Wu, Wei Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3274-3283

Understanding Noise Injection in GANs

Ruili Feng, Deli Zhao, Zheng-Jun Zha ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3284-3293

GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings

Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3294-3304

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3305-3317

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

Marc Finzi, Max Welling, Andrew Gordon Wilson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3318-3328

Few-Shot Conformal Prediction with Auxiliary Tasks

Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3329-3339

Scalable Certified Segmentation via Randomized Smoothing

Marc Fischer, Maximilian Baader, Martin Vechev ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3340-3351

What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules

Jonas Fischer, Anna Olah, Jilles Vreeken ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3352-3362

Online Learning with Optimism and Delay

Genevieve E Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3363-3373

Online A-Optimal Design and Active Linear Regression

Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3374-3383

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

Adam Foster, Desi R Ivanova, Ilyas Malik, Tom Rainforth ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3384-3395

Efficient Online Learning for Dynamic k-Clustering

Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3396-3406

Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning

Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3407-3416

Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins

Spencer Frei, Yuan Cao, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3417-3426

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise

Spencer Frei, Yuan Cao, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3427-3438

Post-selection inference with HSIC-Lasso

Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3439-3448

Variational Data Assimilation with a Learned Inverse Observation Operator

Thomas Frerix, Dmitrii Kochkov, Jamie Smith, Daniel Cremers, Michael Brenner, Stephan Hoyer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3449-3458

Bayesian Quadrature on Riemannian Data Manifolds

Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3459-3468

Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing

Cheng Fu, Hanxian Huang, Xinyun Chen, Yuandong Tian, Jishen Zhao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3469-3479

Learning Task Informed Abstractions

Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3480-3491

Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference

Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan Lin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3492-3504

Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators

Yonggan Fu, Yongan Zhang, Yang Zhang, David Cox, Yingyan Lin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3505-3517

A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation

Scott Fujimoto, David Meger, Doina Precup ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3518-3529

Learning disentangled representations via product manifold projection

Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodola ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3530-3540

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3541-3552

An Information-Geometric Distance on the Space of Tasks

Yansong Gao, Pratik Chaudhari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3553-3563

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3564-3575

Unsupervised Co-part Segmentation through Assembly

Qingzhe Gao, Bin Wang, Libin Liu, Baoquan Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3576-3586

Discriminative Complementary-Label Learning with Weighted Loss

Yi Gao, Min-Ling Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3587-3597

RATT: Leveraging Unlabeled Data to Guarantee Generalization

Saurabh Garg, Sivaraman Balakrishnan, Zico Kolter, Zachary Lipton ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3598-3609

On Proximal Policy Optimization’s Heavy-tailed Gradients

Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3610-3619

What does LIME really see in images?

Damien Garreau, Dina Mardaoui ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3620-3629

Parametric Graph for Unimodal Ranking Bandit

Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont, Boammani Aser Lompo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3630-3639

Let’s Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework

Floris Geerts, Filip Mazowiecki, Guillermo Perez ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3640-3649

On the difficulty of unbiased alpha divergence minimization

Tomas Geffner, Justin Domke ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3650-3659

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference

Amanda M Gentzel, Purva Pruthi, David Jensen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3660-3671

Strategic Classification in the Dark

Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3672-3681

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL

Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3682-3691

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3692-3701

The Power of Adaptivity for Stochastic Submodular Cover

Rohan Ghuge, Anupam Gupta, Viswanath Nagarajan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3702-3712

Differentially Private Quantiles

Jennifer Gillenwater, Matthew Joseph, Alex Kulesza ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3713-3722

Query Complexity of Adversarial Attacks

Grzegorz Gluch, Rüdiger Urbanke ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3723-3733

Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective

Florin Gogianu, Tudor Berariu, Mihaela C Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3734-3744

12-Lead ECG Reconstruction via Koopman Operators

Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3745-3754

Function Contrastive Learning of Transferable Meta-Representations

Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich, Bernhard Schölkopf ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3755-3765

Active Slices for Sliced Stein Discrepancy

Wenbo Gong, Kaibo Zhang, Yingzhen Li, Jose Miguel Hernandez-Lobato ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3766-3776

On the Problem of Underranking in Group-Fair Ranking

Sruthi Gorantla, Amit Deshpande, Anand Louis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3777-3787

MARINA: Faster Non-Convex Distributed Learning with Compression

Eduard Gorbunov, Konstantin P. Burlachenko, Zhize Li, Peter Richtarik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3788-3798

Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures

Martijn M Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3799-3808

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline

Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3809-3820

Dissecting Supervised Contrastive Learning

Florian Graf, Christoph Hofer, Marc Niethammer, Roland Kwitt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3821-3830

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3831-3841

Detecting Rewards Deterioration in Episodic Reinforcement Learning

Ido Greenberg, Shie Mannor ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3842-3853

Crystallization Learning with the Delaunay Triangulation

Jiaqi Gu, Guosheng Yin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3854-3863

AutoAttend: Automated Attention Representation Search

Chaoyu Guan, Xin Wang, Wenwu Zhu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3864-3874

Operationalizing Complex Causes: A Pragmatic View of Mediation

Limor Gultchin, David Watson, Matt Kusner, Ricardo Silva ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3875-3885

On a Combination of Alternating Minimization and Nesterov’s Momentum

Sergey Guminov, Pavel Dvurechensky, Nazarii Tupitsa, Alexander Gasnikov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3886-3898

Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games

Hongyi Guo, Zuyue Fu, Zhuoran Yang, Zhaoran Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3899-3909

Adversarial Policy Learning in Two-player Competitive Games

Wenbo Guo, Xian Wu, Sui Huang, Xinyu Xing ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3910-3919

Soft then Hard: Rethinking the Quantization in Neural Image Compression

Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3920-3929

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3930-3941

Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting

Chirag Gupta, Aaditya Ramdas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3942-3952

Correcting Exposure Bias for Link Recommendation

Shantanu Gupta, Hao Wang, Zachary Lipton, Yuyang Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3953-3963

The Heavy-Tail Phenomenon in SGD

Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3964-3975

Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks

Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3976-3987

Adapting to Delays and Data in Adversarial Multi-Armed Bandits

Andras Gyorgy, Pooria Joulani ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3988-3997

Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning

Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei, Mahdieh Soleymani Baghshah ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:3998-4007

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4008-4017

Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration

Seungyul Han, Youngchul Sung ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4018-4029

Adversarial Combinatorial Bandits with General Non-linear Reward Functions

Yanjun Han, Yining Wang, Xi Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4030-4039

A Collective Learning Framework to Boost GNN Expressiveness for Node Classification

Mengyue Hang, Jennifer Neville, Bruno Ribeiro ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4040-4050

Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

Austin W. Hanjie, Victor Y Zhong, Karthik Narasimhan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4051-4062

Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient

Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvari, Mengdi Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4063-4073

Bootstrapping Fitted Q-Evaluation for Off-Policy Inference

Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvari, Mengdi Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4074-4084

Compressed Maximum Likelihood

Yi Hao, Alon Orlitsky ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4085-4095

Valid Causal Inference with (Some) Invalid Instruments

Jason S Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4096-4106

Model Performance Scaling with Multiple Data Sources

Tatsunori Hashimoto ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4107-4116

Hierarchical VAEs Know What They Don’t Know

Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4117-4128

SPECTRE: defending against backdoor attacks using robust statistics

Jonathan Hayase, Weihao Kong, Raghav Somani, Sewoong Oh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4129-4139

Boosting for Online Convex Optimization

Elad Hazan, Karan Singh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4140-4149

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models

Chaoyang He, Shen Li, Mahdi Soltanolkotabi, Salman Avestimehr ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4150-4159

SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform

Yuhang He, Niki Trigoni, Andrew Markham ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4160-4170

Logarithmic Regret for Reinforcement Learning with Linear Function Approximation

Jiafan He, Dongruo Zhou, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4171-4180

Finding Relevant Information via a Discrete Fourier Expansion

Mohsen Heidari, Jithin Sreedharan, Gil I Shamir, Wojciech Szpankowski ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4181-4191

Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging

Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4192-4202

Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity

Ryan Henderson, Djork-Arné Clevert, Floriane Montanari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4203-4213

Muesli: Combining Improvements in Policy Optimization

Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado Van Hasselt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4214-4226

Learning Representations by Humans, for Humans

Sophie Hilgard, Nir Rosenfeld, Mahzarin R Banaji, Jack Cao, David Parkes ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4227-4238

Optimizing Black-box Metrics with Iterative Example Weighting

Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Sanmi Koyejo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4239-4249

Trees with Attention for Set Prediction Tasks

Roy Hirsch, Ran Gilad-Bachrach ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4250-4261

Multiplicative Noise and Heavy Tails in Stochastic Optimization

Liam Hodgkinson, Michael Mahoney ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4262-4274

MC-LSTM: Mass-Conserving LSTM

Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey S Nearing, Sepp Hochreiter, Guenter Klambauer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4275-4286

Learning Curves for Analysis of Deep Networks

Derek Hoiem, Tanmay Gupta, Zhizhong Li, Michal Shlapentokh-Rothman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4287-4296

Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes

Peter Holderrieth, Michael J Hutchinson, Yee Whye Teh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4297-4307

Latent Programmer: Discrete Latent Codes for Program Synthesis

Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4308-4318

Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication

Sangwoo Hong, Heecheol Yang, Youngseok Yoon, Taehyun Cho, Jungwoo Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4319-4327

Federated Learning of User Verification Models Without Sharing Embeddings

Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4328-4336

The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets

Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4337-4348

Near-Optimal Representation Learning for Linear Bandits and Linear RL

Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4349-4358

On the Random Conjugate Kernel and Neural Tangent Kernel

Zhengmian Hu, Heng Huang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4359-4368

Off-Belief Learning

Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, Noam Brown, Jakob Foerster ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4369-4379

Generalizable Episodic Memory for Deep Reinforcement Learning

Hao Hu, Jianing Ye, Guangxiang Zhu, Zhizhou Ren, Chongjie Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4380-4390

A Scalable Deterministic Global Optimization Algorithm for Clustering Problems

Kaixun Hua, Mingfei Shi, Yankai Cao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4391-4401

On Recovering from Modeling Errors Using Testing Bayesian Networks

Haiying Huang, Adnan Darwiche ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4402-4411

A Novel Sequential Coreset Method for Gradient Descent Algorithms

Jiawei Huang, Ruomin Huang, Wenjie Liu, Nikolaos Freris, Hu Ding ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4412-4422

FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis

Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4423-4434

STRODE: Stochastic Boundary Ordinary Differential Equation

Hengguan Huang, Hongfu Liu, Hao Wang, Chang Xiao, Ye Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4435-4445

A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance

Minhui Huang, Shiqian Ma, Lifeng Lai ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4446-4455

Projection Robust Wasserstein Barycenters

Minhui Huang, Shiqian Ma, Lifeng Lai ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4456-4465

Accurate Post Training Quantization With Small Calibration Sets

Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4466-4475

Learning and Planning in Complex Action Spaces

Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4476-4486

Generative Adversarial Transformers

Drew A Hudson, Larry Zitnick ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4487-4499

Neural Pharmacodynamic State Space Modeling

Zeshan M Hussain, Rahul G. Krishnan, David Sontag ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4500-4510

Hyperparameter Selection for Imitation Learning

Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphael Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4511-4522

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions

Todd Huster, Jeremy Cohen, Zinan Lin, Kevin Chan, Charles Kamhoua, Nandi O. Leslie, Cho-Yu Jason Chiang, Vyas Sekar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4523-4532

LieTransformer: Equivariant Self-Attention for Lie Groups

Michael J Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4533-4543

Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization

Shahana Ibrahim, Xiao Fu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4544-4554

Selecting Data Augmentation for Simulating Interventions

Maximilian Ilse, Jakub M Tomczak, Patrick Forré ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4555-4562

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Khan Mohammad Emtiyaz ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4563-4573

Active Learning for Distributionally Robust Level-Set Estimation

Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4574-4584

Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization

Hedda Cohen Indelman, Tamir Hazan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4585-4595

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

Shariq Iqbal, Christian A Schroeder De Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4596-4606

Randomized Exploration in Reinforcement Learning with General Value Function Approximation

Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4607-4616

Distributed Second Order Methods with Fast Rates and Compressed Communication

Rustem Islamov, Xun Qian, Peter Richtarik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4617-4628

What Are Bayesian Neural Network Posteriors Really Like?

Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, Andrew Gordon Gordon Wilson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4629-4640

How to Learn when Data Reacts to Your Model: Performative Gradient Descent

Zachary Izzo, Lexing Ying, James Zou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4641-4650

Perceiver: General Perception with Iterative Attention

Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, Joao Carreira ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4651-4664

Imitation by Predicting Observations

Andrew Jaegle, Yury Sulsky, Arun Ahuja, Jake Bruce, Rob Fergus, Greg Wayne ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4665-4676

Local Correlation Clustering with Asymmetric Classification Errors

Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4677-4686

Alternative Microfoundations for Strategic Classification

Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4687-4697

Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free

Ayush Jain, Alon Orlitsky ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4698-4708

Instance-Optimal Compressed Sensing via Posterior Sampling

Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4709-4720

Fairness for Image Generation with Uncertain Sensitive Attributes

Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4721-4732

Feature Clustering for Support Identification in Extreme Regions

Hamid Jalalzai, Rémi Leluc ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4733-4743

Improved Regret Bounds of Bilinear Bandits using Action Space Analysis

Kyoungseok Jang, Kwang-Sung Jun, Se-Young Yun, Wanmo Kang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4744-4754

Inverse Decision Modeling: Learning Interpretable Representations of Behavior

Daniel Jarrett, Alihan Hüyük, Mihaela Van Der Schaar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4755-4771

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization

Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand, Giancarlo B Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J Geras ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4772-4784

Policy Gradient Bayesian Robust Optimization for Imitation Learning

Zaynah Javed, Daniel S Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca Dragan, Ken Goldberg ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4785-4796

In-Database Regression in Input Sparsity Time

Rajesh Jayaram, Alireza Samadian, David Woodruff, Peng Ye ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4797-4806

Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics

Vivek Jayaram, John Thickstun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4807-4818

Objective Bound Conditional Gaussian Process for Bayesian Optimization

Taewon Jeong, Heeyoung Kim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4819-4828

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4829-4838

DeepReDuce: ReLU Reduction for Fast Private Inference

Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4839-4849

Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction

Aditi Jha, Michael J. Morais, Jonathan W Pillow ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4850-4859

Fast margin maximization via dual acceleration

Ziwei Ji, Nathan Srebro, Matus Telgarsky ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4860-4869

Marginalized Stochastic Natural Gradients for Black-Box Variational Inference

Geng Ji, Debora Sujono, Erik B Sudderth ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4870-4881

Bilevel Optimization: Convergence Analysis and Enhanced Design

Kaiyi Ji, Junjie Yang, Yingbin Liang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4882-4892

Efficient Statistical Tests: A Neural Tangent Kernel Approach

Sheng Jia, Ehsan Nezhadarya, Yuhuai Wu, Jimmy Ba ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4893-4903

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, Tom Duerig ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4904-4916

Multi-Dimensional Classification via Sparse Label Encoding

Bin-Bin Jia, Min-Ling Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4917-4926

Self-Damaging Contrastive Learning

Ziyu Jiang, Tianlong Chen, Bobak J Mortazavi, Zhangyang Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4927-4939

Prioritized Level Replay

Minqi Jiang, Edward Grefenstette, Tim Rocktäschel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4940-4950

Monotonic Robust Policy Optimization with Model Discrepancy

Yuankun Jiang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4951-4960

Approximation Theory of Convolutional Architectures for Time Series Modelling

Haotian Jiang, Zhong Li, Qianxiao Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4961-4970

Streaming and Distributed Algorithms for Robust Column Subset Selection

Shuli Jiang, Dennis Li, Irene Mengze Li, Arvind V Mahankali, David Woodruff ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4971-4981

Single Pass Entrywise-Transformed Low Rank Approximation

Yifei Jiang, Yi Li, Yiming Sun, Jiaxin Wang, David Woodruff ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4982-4991

The Emergence of Individuality

Jiechuan Jiang, Zongqing Lu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:4992-5001

Online Selection Problems against Constrained Adversary

Zhihao Jiang, Pinyan Lu, Zhihao Gavin Tang, Yuhao Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5002-5012

Active Covering

Heinrich Jiang, Afshin Rostamizadeh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5013-5022

Emphatic Algorithms for Deep Reinforcement Learning

Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado Van Hasselt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5023-5033

Characterizing Structural Regularities of Labeled Data in Overparameterized Models

Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C Mozer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5034-5044

Optimal Streaming Algorithms for Multi-Armed Bandits

Tianyuan Jin, Keke Huang, Jing Tang, Xiaokui Xiao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5045-5054

Towards Tight Bounds on the Sample Complexity of Average-reward MDPs

Yujia Jin, Aaron Sidford ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5055-5064

Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits

Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5065-5073

MOTS: Minimax Optimal Thompson Sampling

Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5074-5083

Is Pessimism Provably Efficient for Offline RL?

Ying Jin, Zhuoran Yang, Zhaoran Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5084-5096

Adversarial Option-Aware Hierarchical Imitation Learning

Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong, Chuang Gan, Lei Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5097-5106

Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information

Changhun Jo, Kangwook Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5107-5117

Provable Lipschitz Certification for Generative Models

Matt Jordan, Alex Dimakis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5118-5126

Isometric Gaussian Process Latent Variable Model for Dissimilarity Data

Martin Jørgensen, Soren Hauberg ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5127-5136

On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models

Peizhong Ju, Xiaojun Lin, Ness Shroff ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5137-5147

Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits

Kwang-Sung Jun, Lalit Jain, Blake Mason, Houssam Nassif ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5148-5157

Detection of Signal in the Spiked Rectangular Models

Ji Hyung Jung, Hye Won Chung, Ji Oon Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5158-5167

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning

Yonghan Jung, Jin Tian, Elias Bareinboim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5168-5179

A Nullspace Property for Subspace-Preserving Recovery

Mustafa D Kaba, Chong You, Daniel P Robinson, Enrique Mallada, Rene Vidal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5180-5188

Training Recurrent Neural Networks via Forward Propagation Through Time

Anil Kag, Venkatesh Saligrama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5189-5200

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

Peter Kairouz, Ziyu Liu, Thomas Steinke ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5201-5212

Practical and Private (Deep) Learning Without Sampling or Shuffling

Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5213-5225

A Differentiable Point Process with Its Application to Spiking Neural Networks

Hiroshi Kajino ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5226-5235

Projection techniques to update the truncated SVD of evolving matrices with applications

Vasileios Kalantzis, Georgios Kollias, Shashanka Ubaru, Athanasios N. Nikolakopoulos, Lior Horesh, Kenneth Clarkson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5236-5246

Optimal Off-Policy Evaluation from Multiple Logging Policies

Nathan Kallus, Yuta Saito, Masatoshi Uehara ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5247-5256

Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations

Angeliki Kamoutsi, Goran Banjac, John Lygeros ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5257-5268

Statistical Estimation from Dependent Data

Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5269-5278

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes

Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Gordon Wilson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5279-5289

Variational Auto-Regressive Gaussian Processes for Continual Learning

Sanyam Kapoor, Theofanis Karaletsos, Thang D Bui ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5290-5300

Off-Policy Confidence Sequences

Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5301-5310

Learning from History for Byzantine Robust Optimization

Sai Praneeth Karimireddy, Lie He, Martin Jaggi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5311-5319

Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation

Masahiro Kato, Takeshi Teshima ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5320-5333

Improved Algorithms for Agnostic Pool-based Active Classification

Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5334-5344

When Does Data Augmentation Help With Membership Inference Attacks?

Yigitcan Kaya, Tudor Dumitras ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5345-5355

Regularized Submodular Maximization at Scale

Ehsan Kazemi, Shervin Minaee, Moran Feldman, Amin Karbasi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5356-5366

Prior Image-Constrained Reconstruction using Style-Based Generative Models

Varun A Kelkar, Mark Anastasio ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5367-5377

Self Normalizing Flows

Thomas A Keller, Jorn W.T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5378-5387

Interpretable Stability Bounds for Spectral Graph Filters

Henry Kenlay, Dorina Thanou, Xiaowen Dong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5388-5397

Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets

Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien, Damien Scieur ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5398-5408

Markpainting: Adversarial Machine Learning meets Inpainting

David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross Anderson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5409-5419

Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm

Sajad Khodadadian, Zaiwei Chen, Siva Theja Maguluri ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5420-5431

Functional Space Analysis of Local GAN Convergence

Valentin Khrulkov, Artem Babenko, Ivan Oseledets ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5432-5442

"Hey, that’s not an ODE": Faster ODE Adjoints via Seminorms

Patrick Kidger, Ricky T. Q. Chen, Terry J Lyons ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5443-5452

Neural SDEs as Infinite-Dimensional GANs

Patrick Kidger, James Foster, Xuechen Li, Terry J Lyons ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5453-5463

GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

Krishnateja Killamsetty, Durga S, Ganesh Ramakrishnan, Abir De, Rishabh Iyer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5464-5474

Improving Predictors via Combination Across Diverse Task Categories

Kwang In Kim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5475-5485

Self-Improved Retrosynthetic Planning

Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5486-5495

Reward Identification in Inverse Reinforcement Learning

Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5496-5505

I-BERT: Integer-only BERT Quantization

Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5506-5518

Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning

Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J Lim, Byoung-Tak Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5519-5529

Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

Jaehyeon Kim, Jungil Kong, Juhee Son ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5530-5540

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

Dong Ki Kim, Miao Liu, Matthew D Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan How ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5541-5550

Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations

Timothy D. Kim, Thomas Z. Luo, Jonathan W. Pillow, Carlos D. Brody ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5551-5561

The Lipschitz Constant of Self-Attention

Hyunjik Kim, George Papamakarios, Andriy Mnih ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5562-5571

Unsupervised Skill Discovery with Bottleneck Option Learning

Jaekyeom Kim, Seohong Park, Gunhee Kim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5572-5582

ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Wonjae Kim, Bokyung Son, Ildoo Kim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5583-5594

Bias-Robust Bayesian Optimization via Dueling Bandits

Johannes Kirschner, Andreas Krause ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5595-5605

CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients

Dani Kiyasseh, Tingting Zhu, David A Clifton ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5606-5615

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More

Johannes Gasteiger, Marten Lienen, Stephan Günnemann ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5616-5627

Representational aspects of depth and conditioning in normalizing flows

Frederic Koehler, Viraj Mehta, Andrej Risteski ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5628-5636

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5637-5664

One-sided Frank-Wolfe algorithms for saddle problems

Vladimir Kolmogorov, Thomas Pock ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5665-5675

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning

Abi Komanduru, Jean Honorio ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5676-5685

Consensus Control for Decentralized Deep Learning

Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian Stich ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5686-5696

A Distribution-dependent Analysis of Meta Learning

Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5697-5706

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5707-5718

Kernel Stein Discrepancy Descent

Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5719-5730

Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size

Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, Rashmi Vinayak ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5731-5741

NeRF-VAE: A Geometry Aware 3D Scene Generative Model

Adam R Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokra, Danilo Jimenez Rezende ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5742-5752

Active Testing: Sample-Efficient Model Evaluation

Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5753-5763

High Confidence Generalization for Reinforcement Learning

James Kostas, Yash Chandak, Scott M Jordan, Georgios Theocharous, Philip Thomas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5764-5773

Offline Reinforcement Learning with Fisher Divergence Critic Regularization

Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5774-5783

ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks

Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Alexander V Rogozin, Alexander Gasnikov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5784-5793

Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning

Tadashi Kozuno, Yunhao Tang, Mark Rowland, Remi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5794-5804

Adapting to misspecification in contextual bandits with offline regression oracles

Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5805-5814

Out-of-Distribution Generalization via Risk Extrapolation (REx)

David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5815-5826

Near-Optimal Confidence Sequences for Bounded Random Variables

Arun K Kuchibhotla, Qinqing Zheng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5827-5837

Differentially Private Bayesian Inference for Generalized Linear Models

Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5838-5849

Bayesian Structural Adaptation for Continual Learning

Abhishek Kumar, Sunabha Chatterjee, Piyush Rai ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5850-5860

Implicit rate-constrained optimization of non-decomposable objectives

Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5861-5871

A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples

Christian Kümmerle, Claudio M. Verdun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5872-5883

Meta-Thompson Sampling

Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5884-5893

Targeted Data Acquisition for Evolving Negotiation Agents

Minae Kwon, Siddharth Karamcheti, Mariano-Florentino Cuellar, Dorsa Sadigh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5894-5904

ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks

Jungmin Kwon, Jeongseop Kim, Hyunseo Park, In Kwon Choi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5905-5914

On the price of explainability for some clustering problems

Eduardo S Laber, Lucas Murtinho ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5915-5925

Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality

Jonathan Lacotte, Yifei Wang, Mert Pilanci ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5926-5936

Generalization Bounds in the Presence of Outliers: a Median-of-Means Study

Pierre Laforgue, Guillaume Staerman, Stephan Clémençon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5937-5947

Model Fusion for Personalized Learning

Thanh Chi Lam, Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5948-5958

Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix

Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5959-5968

Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions

Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5969-5978

Discovering symbolic policies with deep reinforcement learning

Mikel Landajuela, Brenden K Petersen, Sookyung Kim, Claudio P Santiago, Ruben Glatt, Nathan Mundhenk, Jacob F Pettit, Daniel Faissol ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5979-5989

Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)

Hunter Lang, David Sontag, Aravindan Vijayaraghavan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:5990-5999

Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams

Jan-Hendrik Lange, Paul Swoboda ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6000-6010

CountSketches, Feature Hashing and the Median of Three

Kasper Green Larsen, Rasmus Pagh, Jakub Tětek ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6011-6020

MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space

Sophie C. Laturnus, Philipp Berens ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6021-6031

Improved Regret Bound and Experience Replay in Regularized Policy Iteration

Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6032-6042

LAMDA: Label Matching Deep Domain Adaptation

Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6043-6054

Gaussian Process-Based Real-Time Learning for Safety Critical Applications

Armin Lederer, Alejandro J Ordóñez Conejo, Korbinian A Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6055-6064

Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer

Seungwon Lee, Sima Behpour, Eric Eaton ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6065-6075

Fair Selective Classification Via Sufficiency

Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W Wornell ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6076-6086

On-the-fly Rectification for Robust Large-Vocabulary Topic Inference

Moontae Lee, Sungjun Cho, Kun Dong, David Mimno, David Bindel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6087-6097

Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification

Dong Hoon Lee, Sae-Young Chung ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6098-6108

Continual Learning in the Teacher-Student Setup: Impact of Task Similarity

Sebastian Lee, Sebastian Goldt, Andrew Saxe ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6109-6119

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

Jongmin Lee, Wonseok Jeon, Byungjun Lee, Joelle Pineau, Kee-Eung Kim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6120-6130

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6131-6141

Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously

Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang, Xiaojin Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6142-6151

PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Kimin Lee, Laura M Smith, Pieter Abbeel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6152-6163

Near-Optimal Linear Regression under Distribution Shift

Qi Lei, Wei Hu, Jason Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6164-6174

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems

Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6175-6186

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Joel Z Leibo, Edgar A Dueñez-Guzman, Alexander Vezhnevets, John P Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6187-6199

Better Training using Weight-Constrained Stochastic Dynamics

Benedict Leimkuhler, Tiffany J Vlaar, Timothée Pouchon, Amos Storkey ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6200-6211

Globally-Robust Neural Networks

Klas Leino, Zifan Wang, Matt Fredrikson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6212-6222

Learning to Price Against a Moving Target

Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6223-6232

SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data

Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J Lyons ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6233-6242

Strategic Classification Made Practical

Sagi Levanon, Nir Rosenfeld ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6243-6253

Improved, Deterministic Smoothing for L_1 Certified Robustness

Alexander J Levine, Soheil Feizi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6254-6264

BASE Layers: Simplifying Training of Large, Sparse Models

Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6265-6274

Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models

Jose Lezama, Wei Chen, Qiang Qiu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6275-6285

PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtarik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6286-6295

Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning

Gen Li, Changxiao Cai, Yuxin Chen, Yuantao Gu, Yuting Wei, Yuejie Chi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6296-6306

Winograd Algorithm for AdderNet

Wenshuo Li, Hanting Chen, Mingqiang Huang, Xinghao Chen, Chunjing Xu, Yunhe Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6307-6315

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration

Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Shi Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6316-6325

Privacy-Preserving Feature Selection with Secure Multiparty Computation

Xiling Li, Rafael Dowsley, Martine De Cock ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6326-6336

Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph

Gen Li, Yuantao Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6337-6345

MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning

Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H Pong, Aurick Zhou, Justin Yu, Sergey Levine ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6346-6356

Ditto: Fair and Robust Federated Learning Through Personalization

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6357-6368

Quantization Algorithms for Random Fourier Features

Xiaoyun Li, Ping Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6369-6380

Approximate Group Fairness for Clustering

Bo Li, Lijun Li, Ankang Sun, Chenhao Wang, Yingfan Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6381-6391

Sharper Generalization Bounds for Clustering

Shaojie Li, Yong Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6392-6402

Provably End-to-end Label-noise Learning without Anchor Points

Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6403-6413

A Novel Method to Solve Neural Knapsack Problems

Duanshun Li, Jing Liu, Dongeun Lee, Ali Seyedmazloom, Giridhar Kaushik, Kookjin Lee, Noseong Park ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6414-6424

Mixed Cross Entropy Loss for Neural Machine Translation

Haoran Li, Wei Lu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6425-6436

Training Graph Neural Networks with 1000 Layers

Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6437-6449

Active Feature Acquisition with Generative Surrogate Models

Yang Li, Junier Oliva ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6450-6459

Partially Observed Exchangeable Modeling

Yang Li, Junier Oliva ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6460-6470

Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions

Zhong Li, Minxue Pan, Tian Zhang, Xuandong Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6471-6482

The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks

Xiaocheng Li, Chunlin Sun, Yinyu Ye ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6483-6492

Distributionally Robust Optimization with Markovian Data

Mengmeng Li, Tobias Sutter, Daniel Kuhn ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6493-6503

Communication-Efficient Distributed SVD via Local Power Iterations

Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6504-6514

FILTRA: Rethinking Steerable CNN by Filter Transform

Bo Li, Qili Wang, Gim Hee Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6515-6522

Online Unrelated Machine Load Balancing with Predictions Revisited

Shi Li, Jiayi Xian ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6523-6532

Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator

Zeng Li, Chuanlong Xie, Qinwen Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6533-6542

TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models

Zhuohan Li, Siyuan Zhuang, Shiyuan Guo, Danyang Zhuo, Hao Zhang, Dawn Song, Ion Stoica ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6543-6552

A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance

Xiaoyu Li, Zhenxun Zhuang, Francesco Orabona ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6553-6564

Towards Understanding and Mitigating Social Biases in Language Models

Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, Ruslan Salakhutdinov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6565-6576

Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability

Kaizhao Liang, Jacky Y Zhang, Boxin Wang, Zhuolin Yang, Sanmi Koyejo, Bo Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6577-6587

Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning

Tung-Che Liang, Jin Zhou, Yun-Sheng Chan, Tsung-Yi Ho, Krishnendu Chakrabarty, Cy Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6588-6599

Information Obfuscation of Graph Neural Networks

Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6600-6610

Guided Exploration with Proximal Policy Optimization using a Single Demonstration

Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6611-6620

Debiasing a First-order Heuristic for Approximate Bi-level Optimization

Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Q Davis, Adrian Weller ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6621-6630

Making transport more robust and interpretable by moving data through a small number of anchor points

Chi-Heng Lin, Mehdi Azabou, Eva Dyer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6631-6641

Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation

Xiang Lin, Simeng Han, Shafiq Joty ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6642-6653

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Tao Lin, Sai Praneeth Karimireddy, Sebastian Stich, Martin Jaggi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6654-6665

Generative Causal Explanations for Graph Neural Networks

Wanyu Lin, Hao Lan, Baochun Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6666-6679

Tractable structured natural-gradient descent using local parameterizations

Wu Lin, Frank Nielsen, Khan Mohammad Emtiyaz, Mark Schmidt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6680-6691

Active Learning of Continuous-time Bayesian Networks through Interventions

Dominik Linzner, Heinz Koeppl ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6692-6701

Phase Transitions, Distance Functions, and Implicit Neural Representations

Yaron Lipman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6702-6712

The Earth Mover’s Pinball Loss: Quantiles for Histogram-Valued Regression

Florian List ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6713-6724

Understanding Instance-Level Label Noise: Disparate Impacts and Treatments

Yang Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6725-6735

APS: Active Pretraining with Successor Features

Hao Liu, Pieter Abbeel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6736-6747

Learning by Turning: Neural Architecture Aware Optimisation

Yang Liu, Jeremy Bernstein, Markus Meister, Yisong Yue ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6748-6758

Dynamic Game Theoretic Neural Optimizer

Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6759-6769

Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks

Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6770-6780

Just Train Twice: Improving Group Robustness without Training Group Information

Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6781-6792

Event Outlier Detection in Continuous Time

Siqi Liu, Milos Hauskrecht ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6793-6803

Heterogeneous Risk Minimization

Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6804-6814

Stochastic Iterative Graph Matching

Linfeng Liu, Michael C Hughes, Soha Hassoun, Liping Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6815-6825

Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

Iou-Jen Liu, Unnat Jain, Raymond A Yeh, Alexander Schwing ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6826-6836

Elastic Graph Neural Networks

Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6837-6849

One Pass Late Fusion Multi-view Clustering

Xinwang Liu, Li Liu, Qing Liao, Siwei Wang, Yi Zhang, Wenxuan Tu, Chang Tang, Jiyuan Liu, En Zhu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6850-6859

Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition

Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6860-6870

From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments

Yiwei Liu, Jiamou Liu, Kaibin Wan, Zhan Qin, Zijian Zhang, Bakhadyr Khoussainov, Liehuang Zhu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6871-6881

A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization

Risheng Liu, Xuan Liu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6882-6892

Selfish Sparse RNN Training

Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6893-6904

Temporal Difference Learning as Gradient Splitting

Rui Liu, Alex Olshevsky ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6905-6913

On Robust Mean Estimation under Coordinate-level Corruption

Zifan Liu, Jong Ho Park, Theodoros Rekatsinas, Christos Tzamos ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6914-6924

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

Evan Z Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6925-6935

How Do Adam and Training Strategies Help BNNs Optimization

Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6936-6946

SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis

Yuhan Liu, Shiliang Sun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6947-6956

Learning Deep Neural Networks under Agnostic Corrupted Supervision

Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, Jiayu Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6957-6967

Leveraging Public Data for Practical Private Query Release

Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Steven Wu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6968-6977

Watermarking Deep Neural Networks with Greedy Residuals

Hanwen Liu, Zhenyu Weng, Yuesheng Zhu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6978-6988

Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:6989-7000

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play

Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7001-7010

Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?

Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, Yanzhi Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7011-7020

Group Fisher Pruning for Practical Network Compression

Liyang Liu, Shilong Zhang, Zhanghui Kuang, Aojun Zhou, Jing-Hao Xue, Xinjiang Wang, Yimin Chen, Wenming Yang, Qingmin Liao, Wayne Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7021-7032

Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport

Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7033-7044

Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent

Kangqiao Liu, Liu Ziyin, Masahito Ueda ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7045-7056

Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning

Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C. S. Lui ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7057-7066

Relative Positional Encoding for Transformers with Linear Complexity

Antoine Liutkus, Ondřej Cı́fka, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang, Gael Richard ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7067-7079

Joint Online Learning and Decision-making via Dual Mirror Descent

Alfonso Lobos, Paul Grigas, Zheng Wen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7080-7089

Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach

Federico Lopez, Beatrice Pozzetti, Steve Trettel, Michael Strube, Anna Wienhard ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7090-7101

HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture

Qian Lou, Lei Jiang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7102-7110

Optimal Complexity in Decentralized Training

Yucheng Lu, Christopher De Sa ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7111-7123

DANCE: Enhancing saliency maps using decoys

Yang Young Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7124-7133

Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification

Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7134-7144

Variance Reduced Training with Stratified Sampling for Forecasting Models

Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7145-7155

ACE: Explaining cluster from an adversarial perspective

Yang Young Lu, Timothy C Yu, Giancarlo Bonora, William Stafford Noble ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7156-7167

On Monotonic Linear Interpolation of Neural Network Parameters

James R Lucas, Juhan Bae, Michael R Zhang, Stanislav Fort, Richard Zemel, Roger B Grosse ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7168-7179

Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.

Denis Lukovnikov, Asja Fischer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7180-7191

GraphDF: A Discrete Flow Model for Molecular Graph Generation

Youzhi Luo, Keqiang Yan, Shuiwang Ji ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7192-7203

Trajectory Diversity for Zero-Shot Coordination

Andrei Lupu, Brandon Cui, Hengyuan Hu, Jakob Foerster ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7204-7213

HyperHyperNetwork for the Design of Antenna Arrays

Shahar Lutati, Lior Wolf ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7214-7223

Value Iteration in Continuous Actions, States and Time

Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7224-7234

Meta-Cal: Well-controlled Post-hoc Calibration by Ranking

Xingchen Ma, Matthew B. Blaschko ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7235-7245

Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface

Baorui Ma, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7246-7257

Learning Stochastic Behaviour from Aggregate Data

Shaojun Ma, Shu Liu, Hongyuan Zha, Haomin Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7258-7267

Local Algorithms for Finding Densely Connected Clusters

Peter Macgregor, He Sun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7268-7278

Learning to Generate Noise for Multi-Attack Robustness

Divyam Madaan, Jinwoo Shin, Sung Ju Hwang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7279-7289

Learning Interaction Kernels for Agent Systems on Riemannian Manifolds

Mauro Maggioni, Jason J Miller, Hongda Qiu, Ming Zhong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7290-7300

Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning

Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7301-7312

Domain Generalization using Causal Matching

Divyat Mahajan, Shruti Tople, Amit Sharma ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7313-7324

Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness

Vien V. Mai, Mikael Johansson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7325-7335

Nonparametric Hamiltonian Monte Carlo

Carol Mak, Fabian Zaiser, Luke Ong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7336-7347

Exploiting structured data for learning contagious diseases under incomplete testing

Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John Guttag ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7348-7357

Near-Optimal Algorithms for Explainable k-Medians and k-Means

Konstantin Makarychev, Liren Shan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7358-7367

KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning

Ashok V Makkuva, Xiyang Liu, Mohammad Vahid Jamali, Hessam Mahdavifar, Sewoong Oh, Pramod Viswanath ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7368-7378

Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels

Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7379-7389

Inverse Constrained Reinforcement Learning

Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7390-7399

A Sampling-Based Method for Tensor Ring Decomposition

Osman Asif Malik, Stephen Becker ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7400-7411

Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity

Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7412-7422

Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design

Gustavo Malkomes, Bolong Cheng, Eric H Lee, Mike Mccourt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7423-7434

Consistent Nonparametric Methods for Network Assisted Covariate Estimation

Xueyu Mao, Deepayan Chakrabarti, Purnamrita Sarkar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7435-7446

Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs

Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7447-7458

Adaptive Sampling for Best Policy Identification in Markov Decision Processes

Aymen Al Marjani, Alexandre Proutiere ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7459-7468

Explanations for Monotonic Classifiers.

Joao Marques-Silva, Thomas Gerspacher, Martin C Cooper, Alexey Ignatiev, Nina Narodytska ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7469-7479

Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7480-7491

Blind Pareto Fairness and Subgroup Robustness

Natalia L Martinez, Martin A Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7492-7501

Necessary and sufficient conditions for causal feature selection in time series with latent common causes

Atalanti A Mastakouri, Bernhard Schölkopf, Dominik Janzing ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7502-7511

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt Kusner, Arthur Gretton, Krikamol Muandet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7512-7523

Robust Unsupervised Learning via L-statistic Minimization

Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7524-7533

Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees

Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H Bach, Eli Upfal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7534-7543

Fundamental Tradeoffs in Distributionally Adversarial Training

Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup Rao, Tung Mai ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7544-7554

Leveraging Non-uniformity in First-order Non-convex Optimization

Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7555-7564

Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7565-7577

A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions

Gabriel Mel, Surya Ganguli ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7578-7587

Neural Architecture Search without Training

Joe Mellor, Jack Turner, Amos Storkey, Elliot J Crowley ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7588-7598

Fast active learning for pure exploration in reinforcement learning

Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7599-7608

UCB Momentum Q-learning: Correcting the bias without forgetting

Pierre Menard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7609-7618

An Integer Linear Programming Framework for Mining Constraints from Data

Tao Meng, Kai-Wei Chang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7619-7631

A statistical perspective on distillation

Aditya K Menon, Ankit Singh Rawat, Sashank Reddi, Seungyeon Kim, Sanjiv Kumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7632-7642

Learn2Hop: Learned Optimization on Rough Landscapes

Amil Merchant, Luke Metz, Samuel S Schoenholz, Ekin D Cubuk ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7643-7653

Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Remi Munos ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7654-7664

Provably Efficient Learning of Transferable Rewards

Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7665-7676

Mixed Nash Equilibria in the Adversarial Examples Game

Laurent Meunier, Meyer Scetbon, Rafael B Pinot, Jamal Atif, Yann Chevaleyre ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7677-7687

Learning in Nonzero-Sum Stochastic Games with Potentials

David H Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang, Minne Li, Ying Wen, Joel Jennings, Jun Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7688-7699

EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture

Chenfeng Miao, Liang Shuang, Zhengchen Liu, Chen Minchuan, Jun Ma, Shaojun Wang, Jing Xiao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7700-7709

Outside the Echo Chamber: Optimizing the Performative Risk

John P Miller, Juan C Perdomo, Tijana Zrnic ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7710-7720

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7721-7735

Signatured Deep Fictitious Play for Mean Field Games with Common Noise

Ming Min, Ruimeng Hu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7736-7747

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

Dongchan Min, Dong Bok Lee, Eunho Yang, Sung Ju Hwang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7748-7759

On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks

Hancheng Min, Salma Tarmoun, Rene Vidal, Enrique Mallada ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7760-7768

An Identifiable Double VAE For Disentangled Representations

Graziano Mita, Maurizio Filippone, Pietro Michiardi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7769-7779

Offline Meta-Reinforcement Learning with Advantage Weighting

Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7780-7791

The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization

Taiki Miyagawa, Akinori F Ebihara ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7792-7804

PODS: Policy Optimization via Differentiable Simulation

Miguel Angel Zamora Mora, Momchil Peychev, Sehoon Ha, Martin Vechev, Stelian Coros ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7805-7817

Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games

Dustin Morrill, Ryan D’Orazio, Marc Lanctot, James R Wright, Michael Bowling, Amy R Greenwald ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7818-7828

Neural Rough Differential Equations for Long Time Series

James Morrill, Cristopher Salvi, Patrick Kidger, James Foster ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7829-7838

Connecting Interpretability and Robustness in Decision Trees through Separation

Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7839-7849

Outlier-Robust Optimal Transport

Debarghya Mukherjee, Aritra Guha, Justin M Solomon, Yuekai Sun, Mikhail Yurochkin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7850-7860

Oblivious Sketching for Logistic Regression

Alexander Munteanu, Simon Omlor, David Woodruff ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7861-7871

Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning

Tomoya Murata, Taiji Suzuki ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7872-7881

Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

Kieran A Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7882-7893

No-regret Algorithms for Capturing Events in Poisson Point Processes

Mojmir Mutny, Andreas Krause ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7894-7904

Online Limited Memory Neural-Linear Bandits with Likelihood Matching

Ofir Nabati, Tom Zahavy, Shie Mannor ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7905-7915

Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding

Akira Nakagawa, Keizo Kato, Taiji Suzuki ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7916-7926

GMAC: A Distributional Perspective on Actor-Critic Framework

Daniel W Nam, Younghoon Kim, Chan Y Park ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7927-7936

Memory-Efficient Pipeline-Parallel DNN Training

Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7937-7947

Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering

Shyam Narayanan, Sandeep Silwal, Piotr Indyk, Or Zamir ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7948-7957

Generating images with sparse representations

Charlie Nash, Jacob Menick, Sander Dieleman, Peter Battaglia ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7958-7968

Geometric convergence of elliptical slice sampling

Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7969-7978

HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search

Niv Nayman, Yonathan Aflalo, Asaf Noy, Lihi Zelnik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7979-7990

Emergent Social Learning via Multi-agent Reinforcement Learning

Kamal K Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:7991-8004

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information

Willie Neiswanger, Ke Alexander Wang, Stefano Ermon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8005-8015

Continuous Coordination As a Realistic Scenario for Lifelong Learning

Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath Chandar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8016-8024

Policy Caches with Successor Features

Mark Nemecek, Ronald Parr ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8025-8033

Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners

Elias Chaibub Neto ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8034-8044

Incentivizing Compliance with Algorithmic Instruments

Dung Daniel T Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8045-8055

On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths

Quynh Nguyen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8056-8062

Value-at-Risk Optimization with Gaussian Processes

Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8063-8072

Cross-model Back-translated Distillation for Unsupervised Machine Translation

Xuan-Phi Nguyen, Shafiq Joty, Thanh-Tung Nguyen, Kui Wu, Ai Ti Aw ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8073-8083

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search

Vu Nguyen, Tam Le, Makoto Yamada, Michael A. Osborne ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8084-8095

Interactive Learning from Activity Description

Khanh X Nguyen, Dipendra Misra, Robert Schapire, Miroslav Dudik, Patrick Shafto ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8096-8108

Nonmyopic Multifidelity Acitve Search

Quan Nguyen, Arghavan Modiri, Roman Garnett ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8109-8118

Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks

Quynh Nguyen, Marco Mondelli, Guido F Montufar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8119-8129

Temporal Predictive Coding For Model-Based Planning In Latent Space

Tung D Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8130-8139

Differentially Private Densest Subgraph Detection

Dung Nguyen, Anil Vullikanti ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8140-8151

Data Augmentation for Meta-Learning

Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8152-8161

Improved Denoising Diffusion Probabilistic Models

Alexander Quinn Nichol, Prafulla Dhariwal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8162-8171

Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications

Sloan Nietert, Ziv Goldfeld, Kengo Kato ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8172-8183

AdaXpert: Adapting Neural Architecture for Growing Data

Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu, Yifan Zhang, Yong Guo, Peilin Zhao, Peng Wang, Mingkui Tan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8184-8194

Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction

Kenta Niwa, Guoqiang Zhang, W. Bastiaan Kleijn, Noboru Harada, Hiroshi Sawada, Akinori Fujino ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8195-8204

WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points

Albert No, Taeho Yoon, Kwon Sehyun, Ernest K Ryu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8205-8215

The Impact of Record Linkage on Learning from Feature Partitioned Data

Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8216-8226

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8227-8237

Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods

Chris Nota, Philip Thomas, Bruno C. Da Silva ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8238-8247

Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes

Sebastian W Ober, Laurence Aitchison ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8248-8259

Regularizing towards Causal Invariance: Linear Models with Proxies

Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8260-8270

Sparsity-Agnostic Lasso Bandit

Min-Hwan Oh, Garud Iyengar, Assaf Zeevi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8271-8280

Autoencoder Image Interpolation by Shaping the Latent Space

Alon Oring, Zohar Yakhini, Yacov Hel-Or ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8281-8290

Generalization Guarantees for Neural Architecture Search with Train-Validation Split

Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8291-8301

Vector Quantized Models for Planning

Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aaron Van Den Oord, Oriol Vinyals ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8302-8313

Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling

Ozan Özdenizci, Robert Legenstein ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8314-8324

Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics

Avik Pal, Yingbo Ma, Viral Shah, Christopher V Rackauckas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8325-8335

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8336-8348

Inference for Network Regression Models with Community Structure

Mengjie Pan, Tyler Mccormick, Bailey Fosdick ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8349-8358

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification

Bo Pang, Ying Nian Wu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8359-8370

Leveraging Good Representations in Linear Contextual Bandits

Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8371-8380

Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data

Sung Woo Park, Junseok Kwon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8381-8390

Unsupervised Representation Learning via Neural Activation Coding

Yookoon Park, Sangho Lee, Gunhee Kim, David Blei ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8391-8400

Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8401-8412

Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation

Sung Woo Park, Dong Wook Shu, Junseok Kwon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8413-8421

Optimal Counterfactual Explanations in Tree Ensembles

Axel Parmentier, Thibaut Vidal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8422-8431

PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data

Shreyas Malakarjun Patil, Constantine Dovrolis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8432-8442

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

Anselm Paulus, Michal Rolinek, Vit Musil, Brandon Amos, Georg Martius ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8443-8453

Ensemble Bootstrapping for Q-Learning

Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8454-8463

Homomorphic Sensing: Sparsity and Noise

Liangzu Peng, Boshi Wang, Manolis Tsakiris ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8464-8475

How could Neural Networks understand Programs?

Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8476-8486

Privacy-Preserving Video Classification with Convolutional Neural Networks

Sikha Pentyala, Rafael Dowsley, Martine De Cock ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8487-8499

Rissanen Data Analysis: Examining Dataset Characteristics via Description Length

Ethan Perez, Douwe Kiela, Kyunghyun Cho ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8500-8513

Modelling Behavioural Diversity for Learning in Open-Ended Games

Nicolas Perez-Nieves, Yaodong Yang, Oliver Slumbers, David H Mguni, Ying Wen, Jun Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8514-8524

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8525-8535

Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders

Adeel Pervez, Efstratios Gavves ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8536-8545

Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision

Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8546-8555

Megaverse: Simulating Embodied Agents at One Million Experiences per Second

Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8556-8566

Towards Practical Mean Bounds for Small Samples

My Phan, Philip Thomas, Erik Learned-Miller ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8567-8576

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs

Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8577-8587

GeomCA: Geometric Evaluation of Data Representations

Petra Poklukar, Anastasiia Varava, Danica Kragic ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8588-8598

Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech

Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Kudinov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8599-8608

Bias-Free Scalable Gaussian Processes via Randomized Truncations

Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P Cunningham ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8609-8619

Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset

Ilan Price, Jared Tanner ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8620-8629

BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining

Weizhen Qi, Yeyun Gong, Jian Jiao, Yu Yan, Weizhu Chen, Dayiheng Liu, Kewen Tang, Houqiang Li, Jiusheng Chen, Ruofei Zhang, Ming Zhou, Nan Duan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8630-8639

A Probabilistic Approach to Neural Network Pruning

Xin Qian, Diego Klabjan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8640-8649

Global Prosody Style Transfer Without Text Transcriptions

Kaizhi Qian, Yang Zhang, Shiyu Chang, Jinjun Xiong, Chuang Gan, David Cox, Mark Hasegawa-Johnson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8650-8660

Efficient Differentiable Simulation of Articulated Bodies

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C Lin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8661-8671

Oneshot Differentially Private Top-k Selection

Gang Qiao, Weijie Su, Li Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8672-8681

Density Constrained Reinforcement Learning

Zengyi Qin, Yuxiao Chen, Chuchu Fan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8682-8692

Budgeted Heterogeneous Treatment Effect Estimation

Tian Qin, Tian-Zuo Wang, Zhi-Hua Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8693-8702

Neural Transformation Learning for Deep Anomaly Detection Beyond Images

Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8703-8714

Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions

Shuang Qiu, Xiaohan Wei, Jieping Ye, Zhaoran Wang, Zhuoran Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8715-8725

Optimization Planning for 3D ConvNets

Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Tao Mei ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8726-8736

On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

Shuang Qiu, Jieping Ye, Zhaoran Wang, Zhuoran Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8737-8747

Learning Transferable Visual Models From Natural Language Supervision

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8748-8763

A General Framework For Detecting Anomalous Inputs to DNN Classifiers

Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8764-8775

Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning

Muhammad A Rahman, Niklas Hopner, Filippos Christianos, Stefano V Albrecht ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8776-8786

Decoupling Value and Policy for Generalization in Reinforcement Learning

Roberta Raileanu, Rob Fergus ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8787-8798

Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees

Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M Procopiuc, Claudio Gentile ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8799-8809

Differentially Private Sliced Wasserstein Distance

Alain Rakotomamonjy, Ralaivola Liva ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8810-8820

Zero-Shot Text-to-Image Generation

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8821-8831

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8832-8843

MSA Transformer

Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, Alexander Rives ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8844-8856

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting

Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8857-8868

Generative Particle Variational Inference via Estimation of Functional Gradients

Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8869-8879

Enhancing Robustness of Neural Networks through Fourier Stabilization

Netanel Raviv, Aidan Kelley, Minzhe Guo, Yevgeniy Vorobeychik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8880-8889

Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces

Ankit Singh Rawat, Aditya K Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix Yu, Sashank Reddi, Sanjiv Kumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8890-8901

Cross-domain Imitation from Observations

Dripta S. Raychaudhuri, Sujoy Paul, Jeroen Vanbaar, Amit K. Roy-Chowdhury ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8902-8912

Implicit Regularization in Tensor Factorization

Noam Razin, Asaf Maman, Nadav Cohen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8913-8924

Align, then memorise: the dynamics of learning with feedback alignment

Maria Refinetti, Stéphane D’Ascoli, Ruben Ohana, Sebastian Goldt ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8925-8935

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

Maria Refinetti, Sebastian Goldt, Florent Krzakala, Lenka Zdeborova ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8936-8947

Sharf: Shape-conditioned Radiance Fields from a Single View

Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8948-8958

LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs

Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8959-8970

Interpreting and Disentangling Feature Components of Various Complexity from DNNs

Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8971-8981

Integrated Defense for Resilient Graph Matching

Jiaxiang Ren, Zijie Zhang, Jiayin Jin, Xin Zhao, Sixing Wu, Yang Zhou, Yelong Shen, Tianshi Che, Ruoming Jin, Dejing Dou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8982-8997

Solving high-dimensional parabolic PDEs using the tensor train format

Lorenz Richter, Leon Sallandt, Nikolas Nüsken ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:8998-9009

Best Arm Identification in Graphical Bilinear Bandits

Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, Yann Chevaleyre ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9010-9019

Principled Simplicial Neural Networks for Trajectory Prediction

T. Mitchell Roddenberry, Nicholas Glaze, Santiago Segarra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9020-9029

On Linear Identifiability of Learned Representations

Geoffrey Roeder, Luke Metz, Durk Kingma ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9030-9039

Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data

Esther Rolf, Theodora T Worledge, Benjamin Recht, Michael Jordan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9040-9051

TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL

Clément Romac, Rémy Portelas, Katja Hofmann, Pierre-Yves Oudeyer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9052-9063

Discretization Drift in Two-Player Games

Mihaela C Rosca, Yan Wu, Benoit Dherin, David Barrett ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9064-9074

On the Predictability of Pruning Across Scales

Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9075-9083

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement

Andrew Ross, Finale Doshi-Velez ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9084-9094

Simultaneous Similarity-based Self-Distillation for Deep Metric Learning

Karsten Roth, Timo Milbich, Bjorn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9095-9106

Multi-group Agnostic PAC Learnability

Guy N Rothblum, Gal Yona ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9107-9115

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9116-9126

An Algorithm for Stochastic and Adversarial Bandits with Switching Costs

Chloé Rouyer, Yevgeny Seldin, Nicolò Cesa-Bianchi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9127-9135

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

Yangjun Ruan, Karen Ullrich, Daniel S Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris Maddison ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9136-9147

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9148-9156

Tilting the playing field: Dynamical loss functions for machine learning

Miguel Ruiz-Garcia, Ge Zhang, Samuel S Schoenholz, Andrea J. Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9157-9167

UnICORNN: A recurrent model for learning very long time dependencies

T. Konstantin Rusch, Siddhartha Mishra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9168-9178

Simple and Effective VAE Training with Calibrated Decoders

Oleh Rybkin, Kostas Daniilidis, Sergey Levine ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9179-9189

Model-Based Reinforcement Learning via Latent-Space Collocation

Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9190-9201

Training Data Subset Selection for Regression with Controlled Generalization Error

Durga S, Rishabh Iyer, Ganesh Ramakrishnan, Abir De ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9202-9212

Unsupervised Part Representation by Flow Capsules

Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey Hinton, David J Fleet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9213-9223

Stochastic Sign Descent Methods: New Algorithms and Better Theory

Mher Safaryan, Peter Richtarik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9224-9234

Adversarial Dueling Bandits

Aadirupa Saha, Tomer Koren, Yishay Mansour ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9235-9244

Dueling Convex Optimization

Aadirupa Saha, Tomer Koren, Yishay Mansour ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9245-9254

Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization

Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9255-9264

Asymptotics of Ridge Regression in Convolutional Models

Mojtaba Sahraee-Ardakan, Tung Mai, Anup Rao, Ryan A. Rossi, Sundeep Rangan, Alyson K Fletcher ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9265-9275

Momentum Residual Neural Networks

Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9276-9287

Meta-Learning Bidirectional Update Rules

Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera Y Arcas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9288-9300

Recomposing the Reinforcement Learning Building Blocks with Hypernetworks

Elad Sarafian, Shai Keynan, Sarit Kraus ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9301-9312

Towards Understanding Learning in Neural Networks with Linear Teachers

Roei Sarussi, Alon Brutzkus, Amir Globerson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9313-9322

E(n) Equivariant Graph Neural Networks

Vı́ctor Garcia Satorras, Emiel Hoogeboom, Max Welling ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9323-9332

A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning

Nikunj Saunshi, Arushi Gupta, Wei Hu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9333-9343

Low-Rank Sinkhorn Factorization

Meyer Scetbon, Marco Cuturi, Gabriel Peyré ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9344-9354

Linear Transformers Are Secretly Fast Weight Programmers

Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9355-9366

Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers

Robin M Schmidt, Frank Schneider, Philipp Hennig ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9367-9376

Equivariant message passing for the prediction of tensorial properties and molecular spectra

Kristof Schütt, Oliver Unke, Michael Gastegger ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9377-9388

Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks

Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, Tom Goldstein ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9389-9398

Connecting Sphere Manifolds Hierarchically for Regularization

Damien Scieur, Youngsung Kim ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9399-9409

Learning Intra-Batch Connections for Deep Metric Learning

Jenny Denise Seidenschwarz, Ismail Elezi, Laura Leal-Taixé ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9410-9421

Top-k eXtreme Contextual Bandits with Arm Hierarchy

Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel N Hill, Inderjit S. Dhillon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9422-9433

Pure Exploration and Regret Minimization in Matching Bandits

Flore Sentenac, Jialin Yi, Clement Calauzenes, Vianney Perchet, Milan Vojnovic ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9434-9442

State Entropy Maximization with Random Encoders for Efficient Exploration

Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9443-9454

Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems

Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9455-9464

RRL: Resnet as representation for Reinforcement Learning

Rutav M Shah, Vikash Kumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9465-9476

Equivariant Networks for Pixelized Spheres

Mehran Shakerinava, Siamak Ravanbakhsh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9477-9488

Personalized Federated Learning using Hypernetworks

Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9489-9502

On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise

Jie Shen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9503-9514

Sample-Optimal PAC Learning of Halfspaces with Malicious Noise

Jie Shen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9515-9524

Backdoor Scanning for Deep Neural Networks through K-Arm Optimization

Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, Xiangyu Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9525-9536

State Relevance for Off-Policy Evaluation

Simon P Shen, Yecheng Ma, Omer Gottesman, Finale Doshi-Velez ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9537-9546

SparseBERT: Rethinking the Importance Analysis in Self-attention

Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James Tin-Yau Kwok ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9547-9557

Learning Gradient Fields for Molecular Conformation Generation

Chence Shi, Shitong Luo, Minkai Xu, Jian Tang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9558-9568

Segmenting Hybrid Trajectories using Latent ODEs

Ruian Shi, Quaid Morris ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9569-9579

Deeply-Debiased Off-Policy Interval Estimation

Chengchun Shi, Runzhe Wan, Victor Chernozhukov, Rui Song ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9580-9591

GANMEX: One-vs-One Attributions using GAN-based Model Explainability

Sheng-Min Shih, Pin-Ju Tien, Zohar Karnin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9592-9602

Large-Scale Meta-Learning with Continual Trajectory Shifting

Jaewoong Shin, Hae Beom Lee, Boqing Gong, Sung Ju Hwang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9603-9613

AGENT: A Benchmark for Core Psychological Reasoning

Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua Tenenbaum, Tomer Ullman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9614-9625

Zoo-Tuning: Adaptive Transfer from A Zoo of Models

Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9626-9637

Aggregating From Multiple Target-Shifted Sources

Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagné, Charles X Ling, Boyu Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9638-9648

Testing Group Fairness via Optimal Transport Projections

Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9649-9659

On Characterizing GAN Convergence Through Proximal Duality Gap

Sahil Sidheekh, Aroof Aimen, Narayanan C Krishnan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9660-9670

A Precise Performance Analysis of Support Vector Regression

Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9671-9680

Directed Graph Embeddings in Pseudo-Riemannian Manifolds

Aaron Sim, Maciej L Wiatrak, Angus Brayne, Paidi Creed, Saee Paliwal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9681-9690

Collaborative Bayesian Optimization with Fair Regret

Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9691-9701

Dynamic Planning and Learning under Recovering Rewards

David Simchi-Levi, Zeyu Zheng, Feng Zhu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9702-9711

PopSkipJump: Decision-Based Attack for Probabilistic Classifiers

Carl-Johann Simon-Gabriel, Noman Ahmed Sheikh, Andreas Krause ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9712-9721

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Berfin Simsek, François Ged, Arthur Jacot, Francesco Spadaro, Clement Hongler, Wulfram Gerstner, Johanni Brea ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9722-9732

Flow-based Attribution in Graphical Models: A Recursive Shapley Approach

Raghav Singal, George Michailidis, Hoiyi Ng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9733-9743

Structured World Belief for Reinforcement Learning in POMDP

Gautam Singh, Skand Peri, Junghyun Kim, Hyunseok Kim, Sungjin Ahn ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9744-9755

Skew Orthogonal Convolutions

Sahil Singla, Soheil Feizi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9756-9766

Multi-Task Reinforcement Learning with Context-based Representations

Shagun Sodhani, Amy Zhang, Joelle Pineau ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9767-9779

Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks

Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm H Van Seijen, Mehdi Fatemi, Honglak Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9780-9790

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving

Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9791-9800

PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration

Yuda Song, Wen Sun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9801-9811

Fast Sketching of Polynomial Kernels of Polynomial Degree

Zhao Song, David Woodruff, Zheng Yu, Lichen Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9812-9823

Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums

Chaobing Song, Stephen J Wright, Jelena Diakonikolas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9824-9834

Oblivious Sketching-based Central Path Method for Linear Programming

Zhao Song, Zheng Yu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9835-9847

Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning

Sumedh A Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9848-9858

Decomposed Mutual Information Estimation for Contrastive Representation Learning

Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoff Gordon, Philip Bachman, Remi Tachet Des Combes ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9859-9869

Decoupling Representation Learning from Reinforcement Learning

Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9870-9879

K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets

Xiu Su, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9880-9890

More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method

Kazuya Sugiyama, Vo Nguyen Le Duy, Ichiro Takeuchi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9891-9901

Not All Memories are Created Equal: Learning to Forget by Expiring

Sainbayar Sukhbaatar, Da Ju, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9902-9912

Nondeterminism and Instability in Neural Network Optimization

Cecilia Summers, Michael J. Dinneen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9913-9922

AutoSampling: Search for Effective Data Sampling Schedules

Ming Sun, Haoxuan Dou, Baopu Li, Junjie Yan, Wanli Ouyang, Lei Cui ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9923-9933

What Makes for End-to-End Object Detection?

Peize Sun, Yi Jiang, Enze Xie, Wenqi Shao, Zehuan Yuan, Changhu Wang, Ping Luo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9934-9944

DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9945-9954

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition

Shengyang Sun, Jiaxin Shi, Andrew Gordon Gordon Wilson, Roger B Grosse ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9955-9965

Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

Haitian Sun, Patrick Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W Cohen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9966-9977

PAC-Learning for Strategic Classification

Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9978-9988

Reinforcement Learning for Cost-Aware Markov Decision Processes

Wesley Suttle, Kaiqing Zhang, Zhuoran Yang, Ji Liu, David Kraemer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:9989-9999

Model-Targeted Poisoning Attacks with Provable Convergence

Fnu Suya, Saeed Mahloujifar, Anshuman Suri, David Evans, Yuan Tian ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10000-10010

Generalization Error Bound for Hyperbolic Ordinal Embedding

Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10011-10021

Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10022-10032

Parallel tempering on optimized paths

Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Cote ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10033-10042

Robust Representation Learning via Perceptual Similarity Metrics

Saeid A Taghanaki, Kristy Choi, Amir Hosein Khasahmadi, Anirudh Goyal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10043-10053

DriftSurf: Stable-State / Reactive-State Learning under Concept Drift

Ashraf Tahmasbi, Ellango Jothimurugesan, Srikanta Tirthapura, Phillip B Gibbons ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10054-10064

Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training

Kai Sheng Tai, Peter D Bailis, Gregory Valiant ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10065-10075

Approximation Theory Based Methods for RKHS Bandits

Sho Takemori, Masahiro Sato ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10076-10085

Supervised Tree-Wasserstein Distance

Yuki Takezawa, Ryoma Sato, Makoto Yamada ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10086-10095

EfficientNetV2: Smaller Models and Faster Training

Mingxing Tan, Quoc Le ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10096-10106

SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples

Anshoo Tandon, Aldric Han, Vincent Tan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10107-10117

1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed

Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10118-10129

Taylor Expansion of Discount Factors

Yunhao Tang, Mark Rowland, Remi Munos, Michal Valko ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10130-10140

REPAINT: Knowledge Transfer in Deep Reinforcement Learning

Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10141-10152

Understanding the Dynamics of Gradient Flow in Overparameterized Linear models

Salma Tarmoun, Guilherme Franca, Benjamin D Haeffele, Rene Vidal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10153-10161

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Bahar Taskesen, Man-Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10162-10172

A Language for Counterfactual Generative Models

Zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10173-10182

Synthesizer: Rethinking Self-Attention for Transformer Models

Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10183-10192

OmniNet: Omnidirectional Representations from Transformers

Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip M Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Donald Metzler ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10193-10202

T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP

Jiaye Teng, Zeren Tan, Yang Yuan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10203-10213

Moreau-Yosida $f$-divergences

Dávid Terjék ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10214-10224

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers

Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C Mozer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10225-10235

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism

Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael Jordan, Ken Goldberg, Joseph Gonzalez ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10236-10246

Monte Carlo Variational Auto-Encoders

Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10247-10257

Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model

Christian B Thygesen, Christian Skjødt Steenmans, Ahmad Salim Al-Sibahi, Lys Sanz Moreta, Anders Bundgård Sørensen, Thomas Hamelryck ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10258-10267

Understanding self-supervised learning dynamics without contrastive pairs

Yuandong Tian, Xinlei Chen, Surya Ganguli ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10268-10278

Online Learning in Unknown Markov Games

Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10279-10288

BORE: Bayesian Optimization by Density-Ratio Estimation

Louis C Tiao, Aaron Klein, Matthias W Seeger, Edwin V. Bonilla, Cedric Archambeau, Fabio Ramos ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10289-10300

Nonparametric Decomposition of Sparse Tensors

Conor Tillinghast, Shandian Zhe ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10301-10311

Probabilistic Programs with Stochastic Conditioning

David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10312-10323

Deep Continuous Networks

Nergis Tomen, Silvia-Laura Pintea, Jan Van Gemert ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10324-10335

Diffusion Earth Mover’s Distance and Distribution Embeddings

Alexander Y Tong, Guillaume Huguet, Amine Natik, Kincaid Macdonald, Manik Kuchroo, Ronald Coifman, Guy Wolf, Smita Krishnaswamy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10336-10346

Training data-efficient image transformers & distillation through attention

Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Herve Jegou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10347-10357

Conservative Objective Models for Effective Offline Model-Based Optimization

Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10358-10368

Sparse within Sparse Gaussian Processes using Neighbor Information

Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10369-10378

SMG: A Shuffling Gradient-Based Method with Momentum

Trang H Tran, Lam M Nguyen, Quoc Tran-Dinh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10379-10389

Bayesian Optimistic Optimisation with Exponentially Decaying Regret

Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10390-10400

On Disentangled Representations Learned from Correlated Data

Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10401-10412

A New Formalism, Method and Open Issues for Zero-Shot Coordination

Johannes Treutlein, Michael Dennis, Caspar Oesterheld, Jakob Foerster ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10413-10423

Learning a Universal Template for Few-shot Dataset Generalization

Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10424-10433

Provable Meta-Learning of Linear Representations

Nilesh Tripuraneni, Chi Jin, Michael Jordan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10434-10443

Cumulants of Hawkes Processes are Robust to Observation Noise

William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10444-10454

PixelTransformer: Sample Conditioned Signal Generation

Shubham Tulsiani, Abhinav Gupta ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10455-10464

A Framework for Private Matrix Analysis in Sliding Window Model

Jalaj Upadhyay, Sarvagya Upadhyay ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10465-10475

Fast Projection Onto Convex Smooth Constraints

Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Levy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10476-10486

SGLB: Stochastic Gradient Langevin Boosting

Aleksei Ustimenko, Liudmila Prokhorenkova ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10487-10496

LTL2Action: Generalizing LTL Instructions for Multi-Task RL

Pashootan Vaezipoor, Andrew C Li, Rodrigo A Toro Icarte, Sheila A. Mcilraith ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10497-10508

Active Deep Probabilistic Subsampling

Hans Van Gorp, Iris Huijben, Bastiaan S Veeling, Nicola Pezzotti, Ruud J. G. Van Sloun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10509-10518

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

Ramakrishna Vedantam, Arthur Szlam, Maximillian Nickel, Ari Morcos, Brenden M Lake ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10519-10529

Towards Domain-Agnostic Contrastive Learning

Vikas Verma, Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc Le ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10530-10541

Sparsifying Networks via Subdifferential Inclusion

Sagar Verma, Jean-Christophe Pesquet ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10542-10552

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

Paul Vicol, Luke Metz, Jascha Sohl-Dickstein ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10553-10563

Online Graph Dictionary Learning

Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10564-10574

Neuro-algorithmic Policies Enable Fast Combinatorial Generalization

Marin Vlastelica, Michal Rolinek, Georg Martius ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10575-10585

Efficient Training of Robust Decision Trees Against Adversarial Examples

Daniël Vos, Sicco Verwer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10586-10595

Object Segmentation Without Labels with Large-Scale Generative Models

Andrey Voynov, Stanislav Morozov, Artem Babenko ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10596-10606

Principal Component Hierarchy for Sparse Quadratic Programs

Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari, Peyman Mohajerin Esfahani ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10607-10616

Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization

Neha Wadia, Daniel Duckworth, Samuel S Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10617-10629

Safe Reinforcement Learning Using Advantage-Based Intervention

Nolan C Wagener, Byron Boots, Ching-An Cheng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10630-10640

Task-Optimal Exploration in Linear Dynamical Systems

Andrew J Wagenmaker, Max Simchowitz, Kevin Jamieson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10641-10652

Learning and Planning in Average-Reward Markov Decision Processes

Yi Wan, Abhishek Naik, Richard S Sutton ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10653-10662

Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces

Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael A. Osborne ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10663-10674

Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model

Zi Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10675-10685

Fairness of Exposure in Stochastic Bandits

Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10686-10696

A Proxy Variable View of Shared Confounding

Yixin Wang, David Blei ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10697-10707

Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss

Jiali Wang, He Chen, Rujun Jiang, Xudong Li, Zihao Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10708-10716

Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework

Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, Wei Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10717-10726

Explainable Automated Graph Representation Learning with Hyperparameter Importance

Xin Wang, Shuyi Fan, Kun Kuang, Wenwu Zhu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10727-10737

Self-Tuning for Data-Efficient Deep Learning

Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10738-10748

Label Distribution Learning Machine

Jing Wang, Xin Geng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10749-10759

AlphaNet: Improved Training of Supernets with Alpha-Divergence

Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10760-10771

Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time

Weichen Wang, Jiequn Han, Zhuoran Yang, Zhaoran Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10772-10782

SG-PALM: a Fast Physically Interpretable Tensor Graphical Model

Yu Wang, Alfred Hero ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10783-10793

Deep Generative Learning via Schrödinger Bridge

Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, Can Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10794-10804

Robust Inference for High-Dimensional Linear Models via Residual Randomization

Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10805-10815

A Modular Analysis of Provable Acceleration via Polyak’s Momentum: Training a Wide ReLU Network and a Deep Linear Network

Jun-Kun Wang, Chi-Heng Lin, Jacob D Abernethy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10816-10827

Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method

Peng Wang, Huikang Liu, Zirui Zhou, Anthony Man-Cho So ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10828-10838

ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation

Mengfan Wang, Boyu Lyu, Guoqiang Yu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10839-10848

The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks

Bohan Wang, Qi Meng, Wei Chen, Tie-Yan Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10849-10858

Robust Learning for Data Poisoning Attacks

Yunjuan Wang, Poorya Mianjy, Raman Arora ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10859-10869

SketchEmbedNet: Learning Novel Concepts by Imitating Drawings

Alexander Wang, Mengye Ren, Richard Zemel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10870-10881

Directional Bias Amplification

Angelina Wang, Olga Russakovsky ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10882-10893

An exact solver for the Weston-Watkins SVM subproblem

Yutong Wang, Clayton Scott ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10894-10904

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

Xiangjun Wang, Junxiao Song, Penghui Qi, Peng Peng, Zhenkun Tang, Wei Zhang, Weimin Li, Xiongjun Pi, Jujie He, Chao Gao, Haitao Long, Quan Yuan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10905-10915

Quantum algorithms for reinforcement learning with a generative model

Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, Martin Roetteler ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10916-10926

Matrix Completion with Model-free Weighting

Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao, Kwun Chuen Gary Chan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10927-10936

UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10937-10947

Instabilities of Offline RL with Pre-Trained Neural Representation

Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, Sham Kakade ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10948-10960

Learning to Weight Imperfect Demonstrations

Yunke Wang, Chang Xu, Bo Du, Honglak Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10961-10970

Evolving Attention with Residual Convolutions

Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10971-10980

Guarantees for Tuning the Step Size using a Learning-to-Learn Approach

Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10981-10990

Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation

Haoxiang Wang, Han Zhao, Bo Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:10991-11002

Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing

Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11003-11012

Robust Asymmetric Learning in POMDPs

Andrew Warrington, Jonathan W Lavington, Adam Scibior, Mark Schmidt, Frank Wood ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11013-11023

A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention

Tomoki Watanabe, Paolo Favaro ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11024-11034

Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond

Dennis Wei ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11035-11046

Inferring serial correlation with dynamic backgrounds

Song Wei, Yao Xie, Dobromir Rahnev ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11047-11057

Meta-learning Hyperparameter Performance Prediction with Neural Processes

Ying Wei, Peilin Zhao, Junzhou Huang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11058-11067

A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting

Eli N Weinstein, Debora Marks ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11068-11079

Thinking Like Transformers

Gail Weiss, Yoav Goldberg, Eran Yahav ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11080-11090

Leveraged Weighted Loss for Partial Label Learning

Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11091-11100

Characterizing the Gap Between Actor-Critic and Policy Gradient

Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11101-11111

Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning

Zixin Wen, Yuanzhi Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11112-11122

Keyframe-Focused Visual Imitation Learning

Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11123-11133

Learning de-identified representations of prosody from raw audio

Jack Weston, Raphael Lenain, Udeepa Meepegama, Emil Fristed ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11134-11145

Solving Inverse Problems with a Flow-based Noise Model

Jay Whang, Qi Lei, Alex Dimakis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11146-11157

Composing Normalizing Flows for Inverse Problems

Jay Whang, Erik Lindgren, Alex Dimakis ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11158-11169

Which transformer architecture fits my data? A vocabulary bottleneck in self-attention

Noam Wies, Yoav Levine, Daniel Jannai, Amnon Shashua ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11170-11181

Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations

Mateusz Wilinski, Andrey Lokhov ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11182-11192

Leveraging Language to Learn Program Abstractions and Search Heuristics

Catherine Wong, Kevin M Ellis, Joshua Tenenbaum, Jacob Andreas ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11193-11204

Leveraging Sparse Linear Layers for Debuggable Deep Networks

Eric Wong, Shibani Santurkar, Aleksander Madry ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11205-11216

Learning Neural Network Subspaces

Mitchell Wortsman, Maxwell C Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11217-11227

Conjugate Energy-Based Models

Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, Jan-Willem Van De Meent ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11228-11239

Making Paper Reviewing Robust to Bid Manipulation Attacks

Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens Van Der Maaten, Kilian Weinberger ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11240-11250

LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning

Yuhuai Wu, Markus N Rabe, Wenda Li, Jimmy Ba, Roger B Grosse, Christian Szegedy ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11251-11262

ChaCha for Online AutoML

Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11263-11273

Temporally Correlated Task Scheduling for Sequence Learning

Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, Tie-Yan Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11274-11284

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels

Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11285-11295

On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP

Tianhao Wu, Yunchang Yang, Simon Du, Liwei Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11296-11306

Generative Video Transformer: Can Objects be the Words?

Yi-Fu Wu, Jaesik Yoon, Sungjin Ahn ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11307-11318

Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11319-11328

Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11329-11339

Data-efficient Hindsight Off-policy Option Learning

Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Siegel, Nicolas Heess, Martin Riedmiller ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11340-11350

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao, Cees Snoek ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11351-11361

On the Optimality of Batch Policy Optimization Algorithms

Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvari, Dale Schuurmans ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11362-11371

CRFL: Certifiably Robust Federated Learning against Backdoor Attacks

Chulin Xie, Minghao Chen, Pin-Yu Chen, Bo Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11372-11382

RNNRepair: Automatic RNN Repair via Model-based Analysis

Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, Xinyu Xing ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11383-11392

Deep Reinforcement Learning amidst Continual Structured Non-Stationarity

Annie Xie, James Harrison, Chelsea Finn ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11393-11403

Batch Value-function Approximation with Only Realizability

Tengyang Xie, Nan Jiang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11404-11413

Interaction-Grounded Learning

Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11414-11423

Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization

Sang Michael Xie, Tengyu Ma, Percy Liang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11424-11435

Learning While Playing in Mean-Field Games: Convergence and Optimality

Qiaomin Xie, Zhuoran Yang, Zhaoran Wang, Andreea Minca ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11436-11447

Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization

Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11448-11458

A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization

Ran Xin, Usman Khan, Soummya Kar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11459-11469

Explore Visual Concept Formation for Image Classification

Shengzhou Xiong, Yihua Tan, Guoyou Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11470-11479

CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee

Tengyu Xu, Yingbin Liang, Guanghui Lan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11480-11491

To be Robust or to be Fair: Towards Fairness in Adversarial Training

Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, Jiliang Tang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11492-11501

Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold

Wenkai Xu, Takeru Matsuda ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11502-11513

Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives

Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11514-11524

Dash: Semi-Supervised Learning with Dynamic Thresholding

Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11525-11536

An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming

Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11537-11547

Self-supervised Graph-level Representation Learning with Local and Global Structure

Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11548-11558

Conformal prediction interval for dynamic time-series

Chen Xu, Yao Xie ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11559-11569

Learner-Private Convex Optimization

Jiaming Xu, Kuang Xu, Dana Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11570-11580

Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality

Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11581-11591

Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth

Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11592-11602

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11603-11612

KNAS: Green Neural Architecture Search

Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu Sun, Hongxia Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11613-11625

Structured Convolutional Kernel Networks for Airline Crew Scheduling

Yassine Yaakoubi, Francois Soumis, Simon Lacoste-Julien ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11626-11636

Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences

Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11637-11647

EL-Attention: Memory Efficient Lossless Attention for Generation

Yu Yan, Jiusheng Chen, Weizhen Qi, Nikhil Bhendawade, Yeyun Gong, Nan Duan, Ruofei Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11648-11658

Link Prediction with Persistent Homology: An Interactive View

Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11659-11669

CATE: Computation-aware Neural Architecture Encoding with Transformers

Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11670-11681

On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework

Zeyu Yan, Fei Wen, Rendong Ying, Chao Ma, Peilin Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11682-11692

CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Tan, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11693-11703

Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models

Zitong Yang, Yu Bai, Song Mei ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11704-11715

Learning Optimal Auctions with Correlated Valuations from Samples

Chunxue Yang, Xiaohui Bei ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11716-11726

Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks

Greg Yang, Edward J. Hu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11727-11737

LARNet: Lie Algebra Residual Network for Face Recognition

Xiaolong Yang, Xiaohong Jia, Dihong Gong, Dong-Ming Yan, Zhifeng Li, Wei Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11738-11750

BASGD: Buffered Asynchronous SGD for Byzantine Learning

Yi-Rui Yang, Wu-Jun Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11751-11761

Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics

Greg Yang, Etai Littwin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11762-11772

Graph Neural Networks Inspired by Classical Iterative Algorithms

Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11773-11783

Representation Matters: Offline Pretraining for Sequential Decision Making

Mengjiao Yang, Ofir Nachum ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11784-11794

Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies

Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J Ramadge ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11795-11807

Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Chao-Han Huck Yang, Yun-Yun Tsai, Pin-Yu Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11808-11819

When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC

Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao, Qingming Huang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11820-11829

Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss

Xue Yang, Junchi Yan, Qi Ming, Wentao Wang, Xiaopeng Zhang, Qi Tian ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11830-11841

Delving into Deep Imbalanced Regression

Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, Dina Katabi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11842-11851

Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks

Yukun Yang, Wenrui Zhang, Peng Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11852-11862

SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks

Lingxiao Yang, Ru-Yuan Zhang, Lida Li, Xiaohua Xie ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11863-11874

HAWQ-V3: Dyadic Neural Network Quantization

Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael Mahoney, Kurt Keutzer ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11875-11886

Improving Generalization in Meta-learning via Task Augmentation

Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui () Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11887-11897

Deep Learning for Functional Data Analysis with Adaptive Basis Layers

Junwen Yao, Jonas Mueller, Jane-Ling Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11898-11908

Addressing Catastrophic Forgetting in Few-Shot Problems

Pauching Yap, Hippolyt Ritter, David Barber ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11909-11919

Reinforcement Learning with Prototypical Representations

Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11920-11931

Elementary superexpressive activations

Dmitry Yarotsky ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11932-11940

Break-It-Fix-It: Unsupervised Learning for Program Repair

Michihiro Yasunaga, Percy Liang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11941-11952

Improving Gradient Regularization using Complex-Valued Neural Networks

Eric C Yeats, Yiran Chen, Hai Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11953-11963

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11964-11974

From Local Structures to Size Generalization in Graph Neural Networks

Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11975-11986

Improved OOD Generalization via Adversarial Training and Pretraing

Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, Zhiming Ma ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11987-11997

Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints

Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl Johansson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:11998-12008

Continuous-time Model-based Reinforcement Learning

Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12009-12018

Distributed Nyström Kernel Learning with Communications

Rong Yin, Weiping Wang, Dan Meng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12019-12028

Path Planning using Neural A* Search

Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12029-12039

SinIR: Efficient General Image Manipulation with Single Image Reconstruction

Jihyeong Yoo, Qifeng Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12040-12050

Conditional Temporal Neural Processes with Covariance Loss

Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12051-12061

Adversarial Purification with Score-based Generative Models

Jongmin Yoon, Sung Ju Hwang, Juho Lee ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12062-12072

Federated Continual Learning with Weighted Inter-client Transfer

Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, Sung Ju Hwang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12073-12086

Autoencoding Under Normalization Constraints

Sangwoong Yoon, Yung-Kyun Noh, Frank Park ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12087-12097

Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm

Taeho Yoon, Ernest K Ryu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12098-12109

Lower-Bounded Proper Losses for Weakly Supervised Classification

Shuhei M Yoshida, Takashi Takenouchi, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12110-12120

Graph Contrastive Learning Automated

Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12121-12132

LogME: Practical Assessment of Pre-trained Models for Transfer Learning

Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12133-12143

Exponentially Many Local Minima in Quantum Neural Networks

Xuchen You, Xiaodi Wu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12144-12155

DAGs with No Curl: An Efficient DAG Structure Learning Approach

Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12156-12166

Provably Efficient Algorithms for Multi-Objective Competitive RL

Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12167-12176

Whittle Networks: A Deep Likelihood Model for Time Series

Zhongjie Yu, Fabrizio G Ventola, Kristian Kersting ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12177-12186

Deep Latent Graph Matching

Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12187-12197

Learning Generalized Intersection Over Union for Dense Pixelwise Prediction

Jiaqian Yu, Jingtao Xu, Yiwei Chen, Weiming Li, Qiang Wang, Byungin Yoo, Jae-Joon Han ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12198-12207

Large Scale Private Learning via Low-rank Reparametrization

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12208-12218

Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity

Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12219-12229

Neural Tangent Generalization Attacks

Chia-Hung Yuan, Shan-Hung Wu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12230-12240

On Explainability of Graph Neural Networks via Subgraph Explorations

Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12241-12252

Federated Composite Optimization

Honglin Yuan, Manzil Zaheer, Sashank Reddi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12253-12266

Three Operator Splitting with a Nonconvex Loss Function

Alp Yurtsever, Varun Mangalick, Suvrit Sra ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12267-12277

Grey-box Extraction of Natural Language Models

Santiago Zanella-Beguelin, Shruti Tople, Andrew Paverd, Boris Köpf ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12278-12286

Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL

Andrea Zanette ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12287-12297

Learning Binary Decision Trees by Argmin Differentiation

Valentina Zantedeschi, Matt Kusner, Vlad Niculae ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12298-12309

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stephane Deny ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12310-12320

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

Zhanpeng Zeng, Yunyang Xiong, Sathya Ravi, Shailesh Acharya, Glenn M Fung, Vikas Singh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12321-12332

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12333-12344

DORO: Distributional and Outlier Robust Optimization

Runtian Zhai, Chen Dan, Zico Kolter, Pradeep Ravikumar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12345-12355

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?

Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12356-12367

Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons

Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12368-12379

Efficient Lottery Ticket Finding: Less Data is More

Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12380-12390

Robust Policy Gradient against Strong Data Corruption

Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12391-12401

Near Optimal Reward-Free Reinforcement Learning

Zihan Zhang, Simon Du, Xiangyang Ji ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12402-12412

Bayesian Attention Belief Networks

Shujian Zhang, Xinjie Fan, Bo Chen, Mingyuan Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12413-12426

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models

Lily Zhang, Mark Goldstein, Rajesh Ranganath ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12427-12436

Poolingformer: Long Document Modeling with Pooling Attention

Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv, Nan Duan, Weizhu Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12437-12446

Probabilistic Generating Circuits

Honghua Zhang, Brendan Juba, Guy Van Den Broeck ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12447-12457

PAPRIKA: Private Online False Discovery Rate Control

Wanrong Zhang, Gautam Kamath, Rachel Cummings ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12458-12467

Learning from Noisy Labels with No Change to the Training Process

Mingyuan Zhang, Jane Lee, Shivani Agarwal ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12468-12478

Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation

Jiawei Zhang, Linyi Li, Huichen Li, Xiaolu Zhang, Shuang Yang, Bo Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12479-12490

FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning

Tianhao Zhang, Yueheng Li, Chen Wang, Guangming Xie, Zongqing Lu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12491-12500

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization

Yivan Zhang, Gang Niu, Masashi Sugiyama ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12501-12512

Quantile Bandits for Best Arms Identification

Mengyan Zhang, Cheng Soon Ong ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12513-12523

Towards Better Robust Generalization with Shift Consistency Regularization

Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12524-12534

On-Policy Deep Reinforcement Learning for the Average-Reward Criterion

Yiming Zhang, Keith W Ross ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12535-12545

Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution

Zhaoyang Zhang, Wenqi Shao, Jinwei Gu, Xiaogang Wang, Ping Luo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12546-12556

iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients

Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Ehsan M Abbasnejad, Reza Haffari ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12557-12566

Deep Coherent Exploration for Continuous Control

Yijie Zhang, Herke Van Hoof ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12567-12577

Average-Reward Off-Policy Policy Evaluation with Function Approximation

Shangtong Zhang, Yi Wan, Richard S Sutton, Shimon Whiteson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12578-12588

Matrix Sketching for Secure Collaborative Machine Learning

Mengjiao Zhang, Shusen Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12589-12599

MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration

Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan, Chongjie Zhang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12600-12610

World Model as a Graph: Learning Latent Landmarks for Planning

Lunjun Zhang, Ge Yang, Bradly C Stadie ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12611-12620

Breaking the Deadly Triad with a Target Network

Shangtong Zhang, Hengshuai Yao, Shimon Whiteson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12621-12631

Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference

Shumao Zhang, Pengchuan Zhang, Thomas Y Hou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12632-12641

Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation

Qian Zhang, Yilin Zheng, Jean Honorio ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12642-12652

Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity

Zihan Zhang, Yuan Zhou, Xiangyang Ji ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12653-12662

Learning to Rehearse in Long Sequence Memorization

Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou, Hongxia Yang, Zhou Zhao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12663-12673

Dataset Condensation with Differentiable Siamese Augmentation

Bo Zhao, Hakan Bilen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12674-12685

Joining datasets via data augmentation in the label space for neural networks

Junbo Zhao, Mingfeng Ou, Linji Xue, Yunkai Cui, Sai Wu, Gang Chen ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12686-12696

Calibrate Before Use: Improving Few-shot Performance of Language Models

Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12697-12706

Few-Shot Neural Architecture Search

Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12707-12718

Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks

Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, Da Yan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12719-12735

Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation

Renjie Zheng, Junkun Chen, Mingbo Ma, Liang Huang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12736-12746

Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination

Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12747-12760

How Framelets Enhance Graph Neural Networks

Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, Guido Montufar ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12761-12771

Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions

Zixin Zhong, Wang Chi Cheung, Vincent Tan ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12772-12781

Towards Distraction-Robust Active Visual Tracking

Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12782-12792

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping

Dongruo Zhou, Jiafan He, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12793-12802

Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation

Aurick Zhou, Sergey Levine ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12803-12812

Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations

Fan Zhou, Ping Li ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12813-12823

Incentivized Bandit Learning with Self-Reinforcing User Preferences

Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12824-12834

Towards Defending against Adversarial Examples via Attack-Invariant Features

Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12835-12845

Asymmetric Loss Functions for Learning with Noisy Labels

Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12846-12856

Examining and Combating Spurious Features under Distribution Shift

Chunting Zhou, Xuezhe Ma, Paul Michel, Graham Neubig ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12857-12867

Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm

Mingkang Zhu, Tianlong Chen, Zhangyang Wang ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12868-12877

Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12878-12889

Spectral vertex sparsifiers and pair-wise spanners over distributed graphs

Chunjiang Zhu, Qinqing Liu, Jinbo Bi ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12890-12900

Few-shot Language Coordination by Modeling Theory of Mind

Hao Zhu, Graham Neubig, Yonatan Bisk ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12901-12911

Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels

Zhaowei Zhu, Yiwen Song, Yang Liu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12912-12923

Commutative Lie Group VAE for Disentanglement Learning

Xinqi Zhu, Chang Xu, Dacheng Tao ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12924-12934

Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks

Huiping Zhuang, Zhenyu Weng, Fulin Luo, Toh Kar-Ann, Haizhou Li, Zhiping Lin ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12935-12944

Demystifying Inductive Biases for (Beta-)VAE Based Architectures

Dominik Zietlow, Michal Rolinek, Georg Martius ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12945-12954

Recovering AES Keys with a Deep Cold Boot Attack

Itamar Zimerman, Eliya Nachmani, Lior Wolf ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12955-12966

Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

Matthieu Zimmer, Claire Glanois, Umer Siddique, Paul Weng ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12967-12978

Contrastive Learning Inverts the Data Generating Process

Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12979-12990

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Luisa M Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:12991-13001

Provable Robustness of Adversarial Training for Learning Halfspaces with Noise

Difan Zou, Spencer Frei, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:13002-13011

On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients

Difan Zou, Quanquan Gu ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:13012-13022

A Functional Perspective on Learning Symmetric Functions with Neural Networks

Aaron Zweig, Joan Bruna ; Proceedings of the 38th International Conference on Machine Learning , PMLR 139:13023-13032

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Top 10 Machine Learning Research Papers of 2021

Machine learning research papers showcasing the transformation of the technology

In 2021, machine learning and deep learning had many amazing advances and important research papers may lead to breakthroughs in technology that get used by billions of people. The research in this field is developing very quickly and to help you monitor the progress here is the list of most important recent scientific research papers.

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution

By Paul Vicol, Luke Metz and Jascha Sohl-Dickstein

Presented a technique for fair-minded gradient assessment in untolled calculation charts, called Persistent Evolution Strategies (PES).

PES acquires inclinations from truncated unrolls, which speeds up streamlining by taking into consideration that frequent parameter updates while not experiencing truncation predisposition that influences many contending approaches. The researchers showed PES is extensively relevant, with tests exhibiting its application to an RNN-like task, support learning, etc.

Solving high-dimensional parabolic PDEs using the tensor train format

By Lorenz Richter, Leon Sallandt, and Nikolas Nüsken

Showed tensor trains give an engaging estimate system to illustrative partial differential equations (PDEs): the mix of reformulations as far as in reverse stochastic differential equations and relapse type techniques in the tensor format hold the guarantee of utilizing latent low-rank designs empowering both pressure and effective calculation.

Following this, the scientists have created novel iterative plans including either unequivocal and quick or verifiable and precise updates. Their techniques accomplish an ideal compromise among precision and computational proficiency contrasted and SOTA neural organization-based methodologies.

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Oops I took a gradient: Scalable sampling for discrete distributions

By Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, and Chris J. Maddison

Proposed a general and versatile inexact examining methodology for probabilistic models with discrete factors. Their methodology utilizes angles of the probability work regarding its discrete contributions to propose refreshes in a MetropolisHastings sampler.

The specialists showed observationally that this methodology outflanks conventional samplers in numerous perplexing settings, including Ising models, Potts models, limited Boltzmann machines, and factorial secret Markov models. They additionally exhibited the utilization of their further developed sampler for preparing profound energy-based models (EBM) on high-dimensional discrete information. Further, this methodology outflanks variational auto-encoders and existing EBM.

Optimal complexity in decentralized training

By researchers at Cornell University, Yucheng Lu and Christopher De Sa

Showed how decentralization is a promising strategy for increasing equal machine learning systems. The scientists gave a tight lower bound on the emphasis intricacy for such techniques in a stochastic non-raised setting. The research papers expressed the pinnacle bound uncovered a theoretical gap in the realized assembly pace of many existing decentralized preparing calculations, like D-PSGD. The scientists demonstrated the lower bound is tight and feasible.

The scientists further proposed DeTAG, a useful tattle-style decentralized calculation that accomplishes the lower bound with just a logarithm hole. Exactly, they contrasted DeTaG and other decentralized calculations on picture order assignments and noticed that DeTAG appreciates quicker combination than baselines, particularly on unshuffled information and inadequate networks.

Understanding self-supervised learning dynamics without contrastive pairs

By Facebook AI researchers Yuandong Tian, Xinlei Chen, and Surya Ganguli

Examined different strategies around self-supervised learning (SSL) and proposed an original theoretical methodology, DirectPred that directly sets the straight indicator dependent on the measurements of its inputs, without angle training.

On the ImageNet dataset, it performed equivalently with more unpredictable two-layer non-straight indicators that utilize BatchNorm and beat a direct indicator by 2.5 percent in 300-age preparing (and 5 percent in 60-age). The specialists said DirectPred is persuaded by their theoretical investigation of the non-straight learning elements of non-contrastive SSL in simple linear networks.

How transferable are featured in deep neural networks

By Bengio, Y., Clune, J., Lipson, H., & Yosinski, J.

Evaluated the generality versus particularity of neurons in each layer of a profound convolutional neural network and report a couple of astonishing outcomes. Adaptability is adversely influenced by two unmistakable issues: (1) the specialization of higher layer neurons to their unique errand to the detriment of execution on the objective undertaking, which was normal, and (2) enhancement hardships identified with dividing networks between co-adjusted neurons, which was not normal.

Do we need hundreds of classifiers to solve real-world classification problems?

By Amorim, D.G., Barro, S., Cernadas, E., & Delgado, M.F.

Assessed 179 classifiers emerging from 17 families (discriminant investigation, Bayesian, neural organizations, support vector machines, choice trees, rule-based classifiers, boosting, stacking, arbitrary timberlands and different outfits, summed up direct models, machine learning, closest neighbors, partial least squares and principal segment relapse, calculated and multinomial relapse, various versatile relapse splines, and different strategies).

Knowledge vault: a web-scale approach to probabilistic knowledge fusion

By Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., & Zhang, W.

Introduction of Knowledge Vault, a web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories for constructing knowledge bases. They employ supervised machine learning methods for fusing distinct information sources.

Scalable nearest neighbor algorithms for high dimensional data

By Lowe, D.G., & Muja, M.

New algorithms for rough closest neighbor coordinating and assess and contrast them and past algorithms. To scale to exceptionally enormous informational indexes that would some way or another not fit in the memory of a solitary machine, we propose a disseminated closest neighbor coordinating with a system that can be utilized with any of the algorithms depicted in the research papers.

Trends in extreme learning machines

By Huang, G., Huang, G., Song, S., & You, K.

Presented the research papers showing the status of the theoretical exploration and practical signs of progress on the Extreme learning machine (ELM). Aside from arrangement and relapse, ELM has as of late been reached out for bunching, include determination, illustrative learning, and numerous other learning errands. Because of its momentous productivity, effortlessness, and amazing speculation execution, ELM has been applied in an assortment of areas, like biomedical designing, PC vision, framework ID, and control and advanced mechanics.

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Digital transformation in government has brought an increase in the scale, variety, and complexity of records and greater levels of disorganised data. Current practices for selecting records for transfer to The National Archives (TNA) were developed to deal with paper records and are struggling to deal with this shift. This article examines the background to the problem and outlines a project that TNA undertook to research the feasibility of using commercially available artificial intelligence tools to aid selection. The project AI for Selection evaluated a range of commercial solutions varying from off-the-shelf products to cloud-hosted machine learning platforms, as well as a benchmarking tool developed in-house. Suitability of tools depended on several factors, including requirements and skills of transferring bodies as well as the tools’ usability and configurability. This article also explores questions around trust and explainability of decisions made when using AI for sensitive tasks such as selection.

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Data-driven analysis and machine learning for energy prediction in distributed photovoltaic generation plants: a case study in queensland, australia, modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models, big five personality prediction based in indonesian tweets using machine learning methods.

<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>

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Top Machine Learning Research for the Second Half of 2021

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC December 20, 2021 Daniel Gutierrez, ODSC

As 2021 draws to a close, I’m energized by all the amazing work completed by many prominent research groups extending the state of machine learning in a variety of important directions. In this article, I’ll keep you up to date with my top picks of machine learning research papers for the last half of 2021 that I found particularly compelling and useful. Through my effort to stay current with the field’s research advancement, I found the directions represented in these machine learning research papers to be very promising. I hope you enjoy my selections as much as I have. (Check my recent lists from 2019 , 2020 , and 2021 ).

The Causal Loss: Driving Correlation to Imply Causation

Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance. Although success has been observed in many relevant problems, these algorithms fail when the underlying causality is inconsistent with the assumed relations. This machine learning research paper proposes a novel model-agnostic loss function called Causal Loss that improves the interventional quality of the prediction using an intervened neural-causal regularizer. In support of the theoretical results, the experimental illustration shows how causal loss bestows a non-causal associative model (like a standard neural net or decision tree) with interventional capabilities.

Instance-Conditioned GAN

Generative Adversarial Networks (GANs) can generate near photo-realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as ImageNet and COCO-Stuff remains challenging in unconditional settings. This machine learning research paper takes inspiration from kernel density estimation techniques and introduces a non-parametric approach to modeling distributions of complex datasets. The data manifold is partitioned into a mixture of overlapping neighborhoods described by a data point and its nearest neighbors, and a model is introduced called instance-conditioned GAN (IC-GAN), which learns the distribution around each data point. Experimental results on ImageNet and COCO-Stuff show that IC-GAN significantly improves over unconditional models and unsupervised data partitioning baselines. It’s also shown that IC-GAN can effortlessly transfer to datasets not seen during training by simply changing the conditioning instances, and still generate realistic images. Source code can be found HERE . 

Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values

This paper investigates the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the literature, most of them require a complete training set as input. In practice, data can have missing values, and data missing patterns can depend on group attributes (e.g. gender or race). Simply applying off-the-shelf fair learning algorithms to an imputed dataset may lead to an unfair model. This paper theoretically analyzes different sources of discrimination risks when training with an imputed dataset. An integrated approach is proposed based on decision trees that does not require a separate process of imputation and learning. Instead, a tree is trained with missing incorporated as attribute (MIA), which does not require explicit imputation, and a fairness-regularized objective function is optimized. The approach outperforms existing fairness intervention methods applied to an imputed dataset, through several experiments on real-world datasets.

Merlion: A Machine Learning Library for Time Series

This paper introduces Merlion , an open-source machine learning library for time series. It features a unified interface for many commonly used models and datasets for anomaly detection and forecasting on both univariate and multivariate time series, along with standard pre/post-processing layers. It has several modules to improve ease-of-use, including visualization, anomaly score calibration to improve interpetability, AutoML for hyperparameter tuning and model selection, and model ensembling. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a model in production. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs and benchmark them across multiple time series datasets. The paper highlights Merlion’s architecture and major functionalities, and a report of benchmark numbers across different baseline models and ensembles.

A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches

Computational methods to model political bias in social media involve several challenges due to heterogeneity, high-dimensional, multiple modalities, and the scale of the data. Political bias in social media has been studied in multiple viewpoints like media bias, political ideology, echo chambers, and controversies using machine learning pipelines. Most of the current methods rely heavily on the manually-labeled ground-truth data for the underlying political bias prediction tasks. Limitations of such methods include human-intensive labeling, labels related to only a specific problem, and the inability to determine the near future bias state of a social media conversation. This machine learning research paper addresses such problems and gives machine learning approaches to study political bias in two ideologically diverse social media forums: Gab and Twitter without the availability of human-annotated data. 

Sketch Your Own GAN

Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale data set of exemplars and specialized knowledge in deep learning. In contrast, sketching is possibly the most universally accessible way to convey a visual concept. This machine learning research paper presents a method, GAN Sketching, for rewriting GANs with one or more sketches, to make GANs training easier for novice users. In particular, the weights of an original GAN model are changed according to user sketches. The model’s output is encouraged to match the user sketches through a cross-domain adversarial loss. Furthermore, different regularization methods are explored to preserve the original model’s diversity and image quality. Experiments have shown that this method can mold GANs to match shapes and poses specified by sketches while maintaining realism and diversity. Source code can be found HERE . 

Interpretable Propaganda Detection in News Articles

Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter this, a number of approaches have been designed aiming to achieve healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; yet, a major impediment to their broad adoption is that besides being accurate, the decisions of such systems need also to be interpretable in order to be trusted and widely adopted by users. Since misleading and propagandistic content influences readers through the use of a number of deception techniques, this machine learning research paper proposes to detect and to show the use of such techniques as a way to offer interpretability. 

Man versus Machine: AutoML and Human Experts’ Role in Phishing Detection

Machine learning (ML) has developed rapidly in the past few years and has successfully been utilized for a broad range of tasks, including phishing detection. However, building an effective ML-based detection system is not a trivial task, and requires data scientists with knowledge of the relevant domain. Automated Machine Learning (AutoML) frameworks have received a lot of attention in recent years, enabling non-ML experts in building a machine learning model. This brings to an intriguing question of whether AutoML can outperform the results achieved by human data scientists. This machine learning research paper compares the performances of six well-known, state-of-the-art AutoML frameworks on ten different phishing data sets to see whether AutoML-based models can outperform manually crafted machine learning models. The results indicate that AutoML-based models are able to outperform manually developed machine learning models in complex classification tasks, specifically in data sets where the features are not quite discriminative, and datasets with overlapping classes or relatively high degrees of non-linearity. 

Learning with Multiclass AUC: Theory and Algorithms

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. This machine learning research paper starts an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. The paper pays a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, it proposes an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, it is shown that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically; (ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(√(1/N)) result. Practically, to deal with the low scalability of the computational operations, acceleration methods are proposed for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world data sets demonstrate the effectiveness of our proposed framework.

ChainerRL: A Deep Reinforcement Learning Library  

This machine learning research paper introduces ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original machine learning research papers’ experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found HERE .

Subspace Clustering through Sub-Clusters  

The problem of dimension reduction is of increasing importance in modern data analysis. This machine learning research paper considers modeling the collection of points in a high dimensional space as a union of low dimensional subspaces. In particular, a highly scalable sampling based algorithm is proposed that clusters the entire data via first spectral clustering of a small random sample followed by classifying or labeling the remaining out-of-sample points. The key idea is that this random subset borrows information across the entire dataset and that the problem of clustering points can be replaced with the more efficient problem of “clustering sub-clusters”. Theoretical guarantees for the procedure are provided. The numerical results indicate that for large datasets the proposed algorithm outperforms other state-of-the-art subspace clustering algorithms with respect to accuracy and speed.

LassoNet: Neural Networks with Feature Sparsity

Much work has been done recently to make neural networks more interpretable, and one approach is to arrange for the network to use only a subset of the available features. In linear models, Lasso (or ℓ1-regularized) regression assigns zero weights to the most irrelevant or redundant features, and is widely used in data science. However the Lasso only applies to linear models. Here we introduce LassoNet, a neural network framework with global feature selection. The approach outlined in this machine learning research paper achieves feature sparsity by allowing a feature to participate in a hidden unit only if its linear representative is active. Unlike other approaches to feature selection for neural nets, the method uses a modified objective function with constraints, and so integrates feature selection with the parameter learning directly. As a result, it delivers an entire regularization path of solutions with a range of feature sparsity. In experiments with real and simulated data, LassoNet significantly outperforms state-of-the-art methods for feature selection and regression. The LassoNet method uses projected proximal gradient descent, and generalizes directly to deep networks. It can be implemented by adding just a few lines of code to a standard neural network.

Interpretable Random Forests via Rule Extraction 

This machine learning research paper introduces SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm, which takes the form of a short and simple list of rules. State-of-the-art learning algorithms are often referred to as “black boxes” because of the high number of operations involved in their prediction process. Despite their powerful predictivity, this lack of interpretability may be highly restrictive for applications with critical decisions at stake. On the other hand, algorithms with a simple structure—typically decision trees, rule algorithms, or sparse linear models—are well known for their instability. This undesirable feature makes the conclusions of the data analysis unreliable and turns out to be a strong operational limitation. This motivates the design of SIRUS, based on random forests, which combines a simple structure, a remarkable stable behavior when data is perturbed, and an accuracy comparable to its competitors. The efficiency of the method both empirically (through experiments) and theoretically (with the proof of its asymptotic stability) is demonstrated. An R/C++ software implementation sirus is available from CRAN .

KML – Using Machine Learning to Improve Storage Systems

Operating systems include many heuristic algorithms designed to improve overall storage performance and throughput. Because such heuristics cannot work well for all conditions and workloads, system designers resorted to exposing numerous tunable parameters to users – essentially burdening users with continually optimizing their own storage systems and applications. Storage systems are usually responsible for most latency in I/O heavy applications, so even a small overall latency improvement can be significant. Machine learning (ML) techniques promise to learn patterns, generalize from them, and enable optimal solutions that adapt to changing workloads. This machine learning research paper proposes that ML solutions become the first-class component in OSs and replace manual heuristics to optimize storage systems dynamically. The machine learning research paper describes a proposed ML architecture, called KML. The researchers developed a prototype KML architecture and applied it to two problems: optimal readahead and NFS read-size values. The experiments show that KML consumes little OS resources, adds negligible latency, and yet can learn patterns that can improve I/O throughput by as much as 2.3x or 15x for the two use cases respectively – even for complex, never-before-seen, concurrently running mixed workloads on different storage devices.

Feature selection or extraction decision process for clustering using PCA and FRSD

This machine learning research paper concerns the critical decision process of extracting or selecting the features before applying a clustering algorithm. It is not obvious to evaluate the importance of the features since the most popular methods to do it are usually made for a supervised learning technique process. A clustering algorithm is an unsupervised method. It means that there is no known output label to match the input data. This machine learning research paper proposes a new method to choose the best dimensionality reduction method (selection or extraction) according to the data scientist’s parameters, aiming to apply a clustering process at the end. It uses Feature Ranking Process Based on Silhouette Decomposition (FRSD) algorithm, a Principal Component Analysis (PCA) algorithm, and a K-Means algorithm along with its metric, the Silhouette Index (SI). This machine learning research paper presents 5 use cases based on a smart city dataset. This research also aims to discuss the impacts, advantages, and disadvantages of each choice that can be made in this unsupervised learning process.

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Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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5 Best ML Research Papers At ICML 2021

• Published on July 21, 2021
• by Amit Raja Naik

‘International Conference on Machine Learning,’ has announced the best paper awards. The 38th edition of ICML , one of the fastest-growing artificial intelligence conferences in the world, saw participation from academics, industrial researchers, entrepreneurs, engineers, graduate students and postdocs. 

ICML is renowned for presenting and publishing cutting-edge research on all aspects of machine learning.

Last year, ICML conference attracted close to 4,990 submissions, of which 1088 were accepted, at a 21.8% acceptance rate, lower than the previous year’s 22.6%. In ICML 2020, Google topped the charts of total research papers submitted , followed by DeepMind, Microsoft, Facebook, and Spotify.

Here is the list of papers that won ICML 2021 awards: 

Outstanding paper 

Unbiased gradient estimation in unrolled computation graphs with persistent evolution .

Researchers from Google Brain and the University of Toronto, Paul Vicol, Luke Metz and Jascha Sohl-Dickstein , introduced a method for unbiased gradient estimation in untolled computation graphs, called Persistent Evolution Strategies (PES). 

PES obtains gradients from truncated unrolls, which speeds up optimisation by allowing for frequent parameter updates while not suffering from truncation bias that affects many competing approaches. The researchers showed PES is broadly applicable, with experiments demonstrating its application to an RNN-like task, hyperparameter optimisation, reinforcement learning, and meta-training of learned optimisers. 

Check out the full research paper here . 

Outstanding paper honorable mention

Oops i took a gradient: scalable sampling for discrete distributions.

Google Brain researchers Will Grathwohl , Kevin Swersky , Milad Hashemi , David Duvenaud and Chris J. Maddison proposed a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Their approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a MetropolisHastings sampler. 

The researchers showed empirically that this approach outperforms generic samplers in many complex settings, including Ising models, Potts models, restricted Boltzmann machines, and factorial hidden Markov models. They also demonstrated the use of their improved sampler for training deep energy-based models (EBM) on high dimensional discrete data. Further, this approach outperforms variational auto-encoders and existing EBM.

Check out the full paper here . 

Optimal complexity in decentralised training 

Researchers at Cornell University, Yucheng Lu and Christopher De Sa , showed how decentralisation is a promising method of scaling up parallel machine learning systems.The researchers provided a tight lower bound on the iteration complexity for such methods in a stochastic non-convex setting. 

The paper stated the tower bound revealed a theoretical gap in the known convergence rate of many existing decentralised training algorithms, such as D-PSGD. The researchers proved the lower bound is tight and achievable. 

The researchers further proposed DeTAG, a practical gossip-style decentralised algorithm that achieves the lower bound with only a logarithm gap. Empirically, they compared DeTaG with other decentralised algorithms on image classification tasks and noted that DeTAG enjoys faster convergence than baselines, especially on unshuffled data and sparse networks. 

Understanding self-supervised learning dynamics without contrastive pairs

Facebook AI researchers Yuandong Tian , Xinlei Chen , and Surya Ganguli discussed various methods around self-supervised learning (SSL) and proposed a novel theoretical approach, DirectPred that directly sets the linear predictor based on the statistics of its inputs, without gradient training. 

On the ImageNet dataset, it performed comparably with more complex two-layer non-linear predictors that employ BatchNorm and outperformed a linear predictor by 2.5 percent in 300-epoch training (and 5 percent in 60-epoch). The researchers said DirectPred is motivated by their theoretical study of the non-linear learning dynamics of non-contrastive SSL in simple linear networks. 

Further, the study showed conceptual insights into how non-contrastive SSL methods learn, how they avoided representational collapse, and how multiple factors, like predictor networks, stop-gradients, exponential moving averages, and weight decay all came into play. Finally, the researchers said their simple theory recapitulates the results of real-world ablation studies in both STL-10 and ImageNet. The source code is released on GitHub . 

Congratulations to Facebook AI researchers Yuandong Tian ( @tydsh ), Xinlei Chen ( @endernewton ) & @SuryaGanguli for their #ICML2021 Outstanding Paper Honorable Mention for their work to demystify non-contrastive learning. Learn more: https://t.co/mVgxbBUcnB https://t.co/xpVvlgNOKv — Meta AI (@MetaAI) July 19, 2021

Check out the research paper here . 

Solving high-dimensional parabolic PDEs using the tensor train format

Lorenz Richter , Leon Sallandt, and Nikolas Nüsken showed tensor trains provide an appealing approximation framework for parabolic partial differential equations (PDEs): the combination of reformulations in terms of backward stochastic differential equations and regression-type methods in the tensor format holds the promise of leveraging latent low-rank structures enabling both compression and efficient computation. 

In line with this, the researchers have developed novel iterative schemes involving either explicit and fast or implicit and accurate updates. Their methods achieve a favourable trade-off between accuracy and computational efficiency compared with SOTA neural network-based approaches. 

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13 Research Papers Accepted to ICML 2021

Papers from CS researchers have been accepted to the 38th International Conference on Machine Learning (ICML 2021). 

Associate Professor Daniel Hsu was one of the publication chairs of the conference and Assistant Professor Elham Azizi helped organize the 2021 ICML Workshop on Computational Biology . The workshop highlighted how machine learning approaches can be tailored to making both translational and basic scientific discoveries with biological data.

Below are the abstracts and links to the accepted papers.

A Proxy Variable View of Shared Confounding Yixin Wang Columbia University , David Blei Columbia University

Causal inference from observational data can be biased by unobserved confounders. Confounders—the variables that affect both the treatments and the outcome—induce spurious non-causal correlations between the two. Without additional conditions, unobserved confounders generally make causal quantities hard to identify. In this paper, we focus on the setting where there are many treatments with shared confounding, and we study under what conditions is causal identification possible. The key observation is that we can view subsets of treatments as proxies of the unobserved confounder and identify the intervention distributions of the rest. Moreover, while existing identification formulas for proxy variables involve solving integral equations, we show that one can circumvent the need for such solutions by directly modeling the data. Finally, we extend these results to an expanded class of causal graphs, those with other confounders and selection variables.

Unsupervised Representation Learning via Neural Activation Coding Yookoon Park Columbia University , Sangho Lee Seoul National University , Gunhee Kim Seoul National University , David Blei Columbia University

We present neural activation coding (NAC) as a novel approach for learning deep representations from unlabeled data for downstream applications. We argue that the deep encoder should maximize its nonlinear expressivity on the data for downstream predictors to take full advantage of its representation power. To this end, NAC maximizes the mutual information between activation patterns of the encoder and the data over a noisy communication channel. We show that learning for a noise-robust activation code increases the number of distinct linear regions of ReLU encoders, hence the maximum nonlinear expressivity. More interestingly, NAC learns both continuous and discrete representations of data, which we respectively evaluate on two downstream tasks: (i) linear classification on CIFAR-10 and ImageNet-1K and (ii) nearest neighbor retrieval on CIFAR-10 and FLICKR-25K. Empirical results show that NAC attains better or comparable performance on both tasks over recent baselines including SimCLR and DistillHash. In addition, NAC pretraining provides significant benefits to the training of deep generative models. Our code is available at https://github.com/yookoon/nac.

The Logical Options Framework Brandon Araki MIT , Xiao Li MIT , Kiran Vodrahalli Columbia University , Jonathan DeCastro Toyota Research Institute , Micah Fry MIT Lincoln Laboratory , Daniela Rus MIT CSAIL

Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF’s learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps on our benchmarks. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning Yonghan Jung Columbia University , Jin Tian Columbia University , Elias Bareinboim Columbia University

General methods have been developed for estimating causal effects from observational data under causal assumptions encoded in the form of a causal graph. Most of this literature assumes that the underlying causal graph is completely specified. However, only observational data is available in most practical settings, which means that one can learn at most a Markov equivalence class (MEC) of the underlying causal graph. In this paper, we study the problem of causal estimation from a MEC represented by a partial ancestral graph (PAG), which is learnable from observational data. We develop a general estimator for any identifiable causal effects in a PAG. The result fills a gap for an end-to-end solution to causal inference from observational data to effects estimation. Specifically, we develop a complete identification algorithm that derives an influence function for any identifiable causal effects from PAGs. We then construct a double/debiased machine learning (DML) estimator that is robust to model misspecification and biases in nuisance function estimation, permitting the use of modern machine learning techniques. Simulation results corroborate with the theory.

Environment Inference for Invariant Learning  Elliot Creager University of Toronto , Joern Jacobsen Apple Inc. , Richard Zemel Columbia University

Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumption in this area is that the training examples are partitioned into  domains'' or environments”. Our focus is on the more common setting where such partitions are not provided. We propose EIIL, a general framework for domain-invariant learning that incorporates Environment Inference to directly infer partitions that are maximally informative for downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on the CMNIST benchmark without using environment labels, and significantly outperforms ERM on worst-group performance in the Waterbirds dataset. Finally, we establish connections between EIIL and algorithmic fairness, which enables EIIL to improve accuracy and calibration in a fair prediction problem.

SketchEmbedNet: Learning Novel Concepts by Imitating Drawings Alex Wang University of Toronto , Mengye Ren University of Toronto , Richard Zemel Columbia University

Sketch drawings capture the salient information of visual concepts. Previous work has shown that neural networks are capable of producing sketches of natural objects drawn from a small number of classes. While earlier approaches focus on generation quality or retrieval, we explore properties of image representations learned by training a model to produce sketches of images. We show that this generative, class-agnostic model produces informative embeddings of images from novel examples, classes, and even novel datasets in a few-shot setting. Additionally, we find that these learned representations exhibit interesting structure and compositionality.

Universal Template for Few-Shot Dataset Generalization Eleni Triantafillou University of Toronto , Hugo Larochelle Google Brain , Richard Zemel Columbia University , Vincent Dumoulin Google

Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from \emph{new datasets} using only a few examples. To this end, we propose to utilize the diverse training set to construct a \emph{universal template}: a partial model that can define a wide array of dataset-specialized models, by plugging in appropriate components. For each new few-shot classification problem, our approach therefore only requires inferring a small number of parameters to insert into the universal template. We design a separate network that produces an initialization of those parameters for each given task, and we then fine-tune its proposed initialization via a few steps of gradient descent. Our approach is more parameter-efficient, scalable and adaptable compared to previous methods, and achieves the state-of-the-art on the challenging Meta-Dataset benchmark.

On Monotonic Linear Interpolation of Neural Network Parameters James Lucas University of Toronto , Juhan Bae University of Toronto, Michael Zhang University of Toronto , Stanislav Fort Google AI , Richard Zemel Columbia University , Roger Grosse University of Toronto

Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decrease in the training objective. This Monotonic Linear Interpolation (MLI) property, first observed by Goodfellow et al. 2014, persists in spite of the non-convex objectives and highly non-linear training dynamics of neural networks. Extending this work, we evaluate several hypotheses for this property that, to our knowledge, have not yet been explored. Using tools from differential geometry, we draw connections between the interpolated paths in function space and the monotonicity of the network — providing sufficient conditions for the MLI property under mean squared error. While the MLI property holds under various settings (e.g., network architectures and learning problems), we show in practice that networks violating the MLI property can be produced systematically, by encouraging the weights to move far from initialization. The MLI property raises important questions about the loss landscape geometry of neural networks and highlights the need to further study their global properties.

A Computational Framework For Slang Generation Zhewei Sun University of Toronto , Richard Zemel Columbia University , Yang Xu University of Toronto

Slang is a common type of informal language, but its flexible nature and paucity of data resources present challenges for existing natural language systems. We take an initial step toward machine generation of slang by developing a framework that models the speaker’s word choice in slang context. Our framework encodes novel slang meaning by relating the conventional and slang senses of a word while incorporating syntactic and contextual knowledge in slang usage. We construct the framework using a combination of probabilistic inference and neural contrastive learning. We perform rigorous evaluations on three slang dictionaries and show that our approach not only outperforms state-of-the-art language models, but also better predicts the historical emergence of slang word usages from 1960s to 2000s. We interpret the proposed models and find that the contrastively learned semantic space is sensitive to the similarities between slang and conventional senses of words. Our work creates opportunities for the automated generation and interpretation of informal language.

Wandering Within A World: Online Contextualized Few-Shot Learning Mengye Ren University of Toronto , Michael Iuzzolino Google Research , Michael Mozer Google Research , Richard Zemel Columbia University

We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and instead models are evaluated online while learning novel classes. As in the real world, where the presence of spatiotemporal context helps us retrieve learned skills in the past, our online few-shot learning setting also features an underlying context that changes throughout time. Object classes are correlated within a context and inferring the correct context can lead to better performance. Building upon this setting, we propose a new few-shot learning dataset based on large scale indoor imagery that mimics the visual experience of an agent wandering within a world. Furthermore, we convert popular few-shot learning approaches into online versions and we also propose a new contextual prototypical memory model that can make use of spatiotemporal contextual information from the recent past.

Bayesian Few-Shot Classification With One-Vs-Each Polya-Gamma Augmented Gaussian Processes Jake Snell University of Toronto , Richard Zemel Columbia University

Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. Bayesian methods are well-suited to tackling the fundamental issue of overfitting in the few-shot scenario because they allow practitioners to specify prior beliefs and update those beliefs in light of observed data. Contemporary approaches to Bayesian few-shot classification maintain a posterior distribution over model parameters, which is slow and requires storage that scales with model size. Instead, we propose a Gaussian process classifier based on a novel combination of Pólya-Gamma augmentation and the one-vs-each softmax approximation that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks.

Theoretical Bounds On Estimation Error For Meta-Learning James Lucas University of Toronto , Mengye Ren University of Toronto , Irene Kameni African Master for Mathematical Sciences , Toni Pitassi Columbia University , Richard Zemel Columbia University

Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be adapted to more realistic settings where train and test distributions differ. Unfortunately, there is severely limited theoretical support for these algorithms and little is known about the difficulty of these problems. In this work, we provide novel information-theoretic lower-bounds on minimax rates of convergence for algorithms that are trained on data from multiple sources and tested on novel data. Our bounds depend intuitively on the information shared between sources of data, and characterize the difficulty of learning in this setting for arbitrary algorithms. We demonstrate these bounds on a hierarchical Bayesian model of meta-learning, computing both upper and lower bounds on parameter estimation via maximum-a-posteriori inference.

A PAC-Bayesian Approach To Generalization Bounds For Graph Neural Networks Renjie Liao University of Toronto , Raquel Urtasun University of Toronto , Richard Zemel Columbia University

In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPGNNs), via a PAC-Bayesian approach. Our result reveals that the maximum node degree and spectral norm of the weights govern the generalization bounds of both models. We also show that our bound for GCNs is a natural generalization of the results developed in  arXiv:1707.09564v2  [cs.LG] for fully-connected and convolutional neural networks. For message passing GNNs, our PAC-Bayes bound improves over the Rademacher complexity based bound in  arXiv:2002.06157v1  [cs.LG], showing a tighter dependency on the maximum node degree and the maximum hidden dimension. The key ingredients of our proofs are a perturbation analysis of GNNs and the generalization of PAC-Bayes analysis to non-homogeneous GNNs. We perform an empirical study on several real-world graph datasets and verify that our PAC-Bayes bound is tighter than others.

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Upcoming events, in the news, press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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Machine Learning Tech Giants: What are the Current Trends in Research?

24 Pages Posted: 4 May 2024

Assel Omarova

Sorbonne University; University of Technology of Compiegne (UTC); University of Turin; Nazarbayev University

Fazli Shermatov

Sorbonne University; Universita degli Studi di Torino; University of Technology of Compiegne (UTC); Université Paris Cité

Gabriela Fuentes

Sorbonne University; University of Technology of Compiegne (UTC); University of Turin

Date Written: April 12, 2024

Artificial Intelligence (AI) and Machine Learning (ML) are driving a transformative revolution across industries and society. AI, simulating human intelligence, and ML, its backbone, enable machines to learn, predict, and improve. Tech giants like Google and Facebook harness AI for digital advertising, utilising vast datasets to understand and engage audiences. ML's role in pattern recognition without explicit programming enhances efficiency and innovation. Major companies like Microsoft, Google, and Amazon, with exabytes of data, invest significantly in AI R&D. Google and Facebook's 2021 R&D budgets surpassed the R&D spending of many countries. Google and Microsoft lead in extensive R&D networks, reflecting AI's dynamic evolution. Machine Learning, particularly Deep Learning (DL) and Computer Vision, has seen remarkable growth. DL, a subset of ML, powers AI applications like Apple's Siri and Google's Assistant. Computer Vision, pivotal for image recognition, gained prominence with breakthroughs in 2012. Our research delves into these domains, providing insights beyond GAFAM (Google, Apple, Facebook, Amazon, Microsoft) and exploring contributions from other big players like NVIDIA and Intel. Methodologically, we analyse 1,907 ML-related papers and 6,490 patents funded by seven major companies from 2008 to 2023, utilising sentiment analysis, multi-term phrase extraction, and semantic profiling to reveal relationships between related topics within and among these corporations. Technological cooperation simulations showcase the potential impact on patent production. Cooperation yields more patents than competition or oligopoly scenarios, emphasizing the benefits of collaborative innovation for the potential development of new technologies but with the detrimental consequence of more concentration of knowledge and reinforcement of intellectual monopolies. Public policy implications suggest a shift towards open science and markets, democratising knowledge access and fostering global cooperation. Our research provides a holistic view of the evolving AI and ML landscape, emphasising the importance of collaboration, technological trends, and public policies for a sustainable and inclusive future.

Keywords: Artificial inteligence, machine learning, Tech giants, GAFAM

JEL Classification: O31, O32, 034

Suggested Citation: Suggested Citation

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University of Technology of Compiegne (UTC) ( email )

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University of Turin ( email )

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Nazarbayev University ( email )

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Fazliddin Shermatov

Sorbonne university, universita degli studi di torino ( email ), university of technology of compiegne (utc), université paris cité.

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Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling

• Cui, Bingyan

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F 1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O 3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting.

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Title: lora: low-rank adaptation of large language models.

Abstract: An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at this https URL .

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1. introduction, 3. discussion, 4. conclusions, acknowledgments, funding information, author contributions, competing interests, data availability, scite: a smart citation index that displays the context of citations and classifies their intent using deep learning.

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Josh M. Nicholson , Milo Mordaunt , Patrice Lopez , Ashish Uppala , Domenic Rosati , Neves P. Rodrigues , Peter Grabitz , Sean C. Rife; scite: A smart citation index that displays the context of citations and classifies their intent using deep learning. Quantitative Science Studies 2021; 2 (3): 882–898. doi: https://doi.org/10.1162/qss_a_00146

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Citation indices are tools used by the academic community for research and research evaluation that aggregate scientific literature output and measure impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they fail to communicate contextual information about a citation. The use of citations in research evaluation without consideration of context can be problematic because a citation that presents contrasting evidence to a paper is treated the same as a citation that presents supporting evidence. To solve this problem, we have used machine learning, traditional document ingestion methods, and a network of researchers to develop a “smart citation index” called scite , which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper and a classification from our deep learning model that indicates whether the statement provides supporting or contrasting evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 25 million full-text scientific articles and currently has a database of more than 880 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation.

https://publons.com/publon/10.1162/qss_a_00146

Citations are a critical component of scientific publishing, linking research findings across time. The first citation index in science, created in 1960 by Eugene Garfield and the Institute for Scientific Information, aimed to “be a spur to many new scientific discoveries in the service of mankind” ( Garfield, 1959 ). Citation indices have facilitated the discovery and evaluation of scientific findings across all fields of research. Citation indices have also led to the establishment of new research fields, such as bibliometrics, scientometrics, and quantitative studies, which have been informative in better understanding science as an enterprise. From these fields have come a variety of citation-based metrics, such as the h -index, a measurement of researcher impact ( Hirsch, 2005 ); the Journal Impact Factor (JIF), a measurement of journal impact ( Garfield, 1955 , 1972 ); and the citation count, a measurement of article impact. Despite the widespread use of bibliometrics, there have been few improvements in citations and citation indices themselves. Such stagnation is partly because citations and publications are largely behind paywalls, making it exceedingly difficult and prohibitively expensive to introduce new innovations in citations or citation indices. This trend is changing, however, with open access publications becoming the standard ( Piwowar, Priem, & Orr, 2019 ) and organizations such as the Initiative for Open Citations ( Initiative for Open Citations, 2017 ; Peroni & Shotton, 2020 ) helping to make citations open. Additionally, with millions of documents being published each year, creating a citation index is a large-scale challenge involving significant financial and computational costs.

Historically, citation indices have only shown the connections between scientific papers without any further contextual information, such as why a citation was made. Because of the lack of context and limited metadata available beyond paper titles, authors, and the date of publications, it has only been possible to calculate how many times a work has been cited, not analyze broadly how it has been cited. This is problematic given citations’ central role in the evaluation of research. In short, not all citations are made equally, yet we have been limited to treating them as such.

Here we describe scite (scite.ai), a new citation index and tool that takes advantage of recent advances in artificial intelligence to produce “Smart Citations.” Smart Citations reveal how a scientific paper has been cited by providing the context of the citation and a classification system describing whether it provides supporting or contrasting evidence for the cited claim, or if it just mentions it.

Such enriched citation information is more informative than a traditional citation index. For example, when Viganó, von Schubert et al. (2018) cites Nicholson, Macedo et al. (2015) , traditional citation indices report this citation by displaying the title of the citing paper and other bibliographic information, such as the journal, year published, and other metadata. Traditional citation indices do not have the capacity to examine contextual information or how the citing paper used the citation, such as whether it was made to support or contrast the findings of the cited paper or if it was made in the introduction or the discussion section of the citing paper. Smart Citations display the same bibliographical information shown in traditional citation indices while providing additional contextual information, such as the citation statement (the sentence containing the in-text citation from the citing article), the citation context (the sentences before and after the citation statement), the location of the citation within the citing article (Introduction, Materials and Methods, Results, Discussion, etc.), the citation type indicating intent (supporting, contrasting, or mentioning), and editorial information from Crossref and PubMed, such as corrections and whether the article has been retracted ( Figure 1 ). Scite previously relied on Retraction Watch data but moved away from this due to licensing issues. Going forward, scite will use its own approach 1 to retraction detection, as well as data from Crossref and PubMed.

Example of scite report page. The scite report page shows citation context, citation type, and various features used to filter and organize this information, including the section where the citation appears in the citing paper, whether or not the citation is a self-citation, and the year of the publication. The example scite report shown in the figure can be accessed at the following link: https://scite.ai/reports/10.7554/elife.05068 .

Adding such information to citation indices has been proposed before. In 1964, Garfield described an “intelligent machine” to produce “citation markers,” such as “critique” or, jokingly, “calamity for mankind” ( Garfield, 1964 ). Citation types describing various uses of citations have been systematically described by Peroni and Shotton in CiTO, the Ci tation T yping O ntology ( Peroni & Shotton, 2012 ). Researchers have used these classifications or variations of them in several bibliometric studies, such as the analysis of citations ( Suelzer, Deal et al., 2019 ) made to the retracted Wakefield paper ( Wakefield, Murch et al., 1998 ), which found most citations to be negative in sentiment. Leung, Macdonald et al. (2017) analyzed the citations made to a five-sentence letter purporting to show opioids as nonaddictive ( Porter & Jick, 1980 ), finding that most citations were uncritically citing the work. Based on these findings, the journal appended a public health warning to the original letter. In addition to citation analyses at the individual article level, citation analyses taking into account the citation type have also been performed on subsets of articles or even entire fields of research. Greenberg (2009) discovered that citations were being distorted, for example being used selectively to exclude contradictory studies to create a false authority in a field of research, a practice carried into grant proposals. Selective citing might be malicious, as suggested in the Greenberg study, but it might also simply reflect sloppy citation practices or citing without reading. Indeed, Letrud and Hernes (2019) recently documented many cases where people were citing reports for the opposite conclusions than the original authors made.

Despite the advantages of citation types, citation classification and analysis require substantial manual effort on the part of researchers to perform even small-scale analyses ( Pride, Knoth, & Harag, 2019 ). Automating the classification of citation types would allow researchers to dramatically expand the scale of citation analyses, thereby allowing researchers to quickly assess large portions of scientific literature. PLOS Labs attempted to enhance citation analysis with the introduction of “rich citations,” which included various additional features to traditional citations such as retraction information and where the citation appeared in the citing paper ( PLOS, 2015 ). However, the project seemed to be mostly a proof of principle, and work on rich citations stopped in 2015, although it is unclear why. Possible reasons that the project did not mature reflect the challenges of accessing the literature at scale, finding a suitable business model for the application, and classifying citation types with the necessary precision and recall for it to be accepted by users. It is only recently that machine learning techniques have evolved to make this task possible, as we demonstrate here. Additional resources, such as the Colil Database ( Fujiwara & Yamamoto, 2015 ) and SciRide Finder ( Volanakis & Krawczyk, 2018 ) both allow users to see the citation context from open access articles indexed in PubMed Central. However, adoption seems to be low for both tools, presumably due to limited coverage of only open access articles. In addition to the development of such tools to augment citation analysis, various researchers have performed automated citation typing. Machine learning was used in early research to identify citation intent ( Teufel, Siddharthan, & Tidhar, 2006 ) and recently Cohan, Ammar et al. (2019) used deep learning techniques. Athar (2011) , Yousif, Niu et al. (2019) , and Yan, Chen, and Li (2020) also used machine learning to identify positive and negative sentiments associated with the citation contexts.

Here, by combining the largest citation type analysis performed to date and developing a useful user interface that takes advantage of the extra contextual information available, we introduce scite, a smart citation index.

2.1. Overview

The retrieval of scientific articles

The identification and matching of in-text citations and references within a scientific article

The matching of references against a bibliographic database

The classification of the citation statements into citation types using deep learning.

The scite ingestion process. Documents are retrieved from the internet, as well as being received through file transfers directly from publishers and other aggregators. They are then processed to identify citations, which are then tied to items in a paper’s reference list. Those citations are then verified, and the information is inserted into scite’s database.

We describe the four components in more detail below.

2.2. Retrieval of Scientific Documents

Access to full-text scientific articles is necessary to extract and classify citation statements and the citation context. We utilize open access repositories such as PubMed Central and a variety of open sources as identified by Unpaywall ( Else, 2018 ), such as open access publishers’ websites, university repositories, and preprint repositories, to analyze open access articles. Other relevant open access document sources, such as Crossref TDM and the Internet Archive have been and are continually evaluated as new sources for document ingestion. Subscription articles used in our analyses have been made available through indexing agreements with over a dozen publishers, including Wiley, BMJ, Karger, Sage, Europe PMC, Thieme, Cambridge University Press, Rockefeller University Press, IOP, Microbiology Society, Frontiers, and other smaller publishers. Once a source of publications is established, documents are retrieved on a regular basis as new articles become available to keep the citation record fresh. Depending on the source, documents may be retrieved and processed anywhere between daily and monthly.

2.3. Identification of In-Text Citations and References from PDF and XML Documents

A large majority of scientific articles are only available as PDF files 2 , a format designed for visual layout and printing, not text-mining. To match and extract citation statements from PDFs with high fidelity, an automated process for converting PDF files into reliable structured content is required. Such conversion is challenging, as it requires identifying in-text citations (the numerical or textual callouts that refer to a particular item in the reference list), identifying and parsing the full bibliographical references in the reference list, linking in-text citations to the correct items in this list, and linking these items to their digital object identifiers (DOIs) in a bibliographic database. As our goal is to eventually process all scientific documents, this process must be scalable and affordable. To accomplish this, we utilize GROBID, an open-source PDF-to-XML converter tool for scientific literature ( Lopez, 2020a ). The goal of GROBID is to automatically convert scholarly PDFs into structured XML representations suitable for large-scale analysis. The structuration process is realized by a cascade of supervised machine learning models. The tool is highly scalable (around five PDF documents per second on a four-core server), is robust, and includes a production-level web API, a Docker image, and benchmarking facilities. GROBID is used by many large scientific information service providers, such as ResearchGate, CERN, and the Internet Archive to support their ingestion and document workflows ( Lopez, 2020a ). The tool is also used for creating machine-friendly data sets of research papers, for instance, the recent CORD-19 data set ( Wang, Lo et al., 2020 ).

Particularly relevant to scite, GROBID was benchmarked as the best open source bibliographical references parser by Tkaczyk, Collins et al. (2018) and has a relatively unique focus on citation context extraction at scale, as illustrated by its usage for building the large-scale Semantic Scholar Open Research Corpus (S2ORC), a corpus of 380.5 million citations, including citation mentions excerpts from the full-text body ( Lo, Wang et al., 2020 ).

In addition to PDFs, some scientific articles are available as XML files, such as the Journal Article Tag Suite (JATS) format. Formatting articles in PDF and XML has become standard practice for most mainstream publishers. While structured XML can solve many issues that need to be addressed with PDFs, XML full texts appear in a variety of different native publisher XML formats, often incomplete and inconsistent from one to another, loosely constrained, and evolving over time into specific versions.

To standardize the variety of XML formats we receive into a common format, we rely upon the open-source tool Pub2TEI ( Lopez, 2020b ). Pub2TEI converts various XML styles from publishers to the same standard TEI format as the one produced by GROBID. This centralizes our document processing across PDF and XML sources.

2.4. Matching References Against the Bibliographic Database Crossref

Once we have identified and matched the in-text citation to an item in a paper’s reference list, this information must be validated. We use an open-source tool, biblio-glutton ( Lopez, 2020c ), which takes a raw bibliographical reference, as well as optionally parsed fields (title, author names, etc.) and matches it against the Crossref database—widely regarded as the industry standard source of ground truth for scholarly publications 3 . The matching accuracy of a raw citation reaches an F-score of 95.4 on a set of 17,015 raw references associated with a DOI, extracted from a data set of 1,943 PMC articles 4 compiled by Constantin (2014) . In an end-to-end perspective, still based on an evaluation with the corpus of 1,943 PMC articles, combining GROBID PDF extraction of citations and bibliographical references with biblio-glutton validations, the pipeline successfully associates around 70% of citation contexts to cited papers with correctly identified DOIs in a given PDF file. When the full-text XML version of an article is available from a publisher, references and linked citation contexts are normally correctly encoded, and the proportion of fully solved citation contexts corresponding to the proportion of cited paper with correctly identified DOIs is around 95% for PMC XML JATS files. The scite platform today only ingests publications with a DOI and only matches references against bibliographical objects with a registered DOI. The given evaluation figures have been calculated relative to these types of citations.

2.5. Task Modeling and Training Data

Extracted citation statements are classified into supporting, contrasting, or mentioning, to identify studies that have tested the claim and to evaluate how a scientific claim has been evaluated in the literature by subsequent research.

We emphasize that scite is not doing sentiment analysis. In natural language processing, sentiment analysis is the study of affective and subjective statements. The most common affective state considered in sentiment analysis is a mere polar view from positive sentiment to negative sentiment, which appeared to be particularly useful in business applications (e.g., product reviews and movie reviews). Following this approach, a subjective polarity can be associated with a citation to try to capture an opinion about the cited paper. The evidence used for sentiment classification relies on the presence of affective words in the citation context, with an associated polarity score capturing the strength of the affective state ( Athar, 2014 ; Halevi & Schimming, 2018 ; Hassan, Imran et al., 2018 ; Yousif et al., 2019 ). Yan et al. (2020) , for instance, use a generic method called SenticNet to identify sentiments in citation contexts extracted from PubMed Central XML files, without particular customization to the scientific domain (only a preprocessing to remove the technical terms from the citation contexts is applied). SenticNet uses a polarity measure associated with 200,000 natural language concepts, propagated to the words and multiword terms realizing these concepts.

In contrast, scite focuses on the authors’ reasons for citing a paper. We use a discrete classification into three discursive functions relative to the scientific debate; see Murray, Lamers et al. (2019) for an example of previous work with typing citations based on rhetorical intention. We consider that for capturing the reliability of a claim, a classification decision into supporting or contrasting must be backed by scientific arguments. The evidence involved in our assessment of citation intent is directed to the factual information presented in the citation context, usually statements about experimental facts and reproducibility results or presentation of a theoretical argument against or agreeing with the cited paper.

Examples of supporting, contrasting, and mentioning citation statements are given in Table 1 , with explanations describing why they are classified as such, including examples where researchers have expressed confusion or disagreement with our classification.

Real-world examples of citation statement classifications with examples explaining why a citation type has or has not been assigned. Citation classifications are based on the following two requirements: there needs to be a written indication that the statement supports or contrasts the cited paper; and there needs to be an indication that it provides evidence for this assertion.

Importantly, just as it is critical to optimize for accuracy of our deep learning model when classifying citations, it is equally important to make sure that the right terminology is used and understood by researchers. We have undergone multiple iterations of the design and display of citation statements and even the words used to define our citation types, including using previous words such as refuting and disputing to describe contrasting citations and confirming to describe supporting citations. The reasons for these changes reflect user feedback expressing confusion over certain terms as well as our intent to limit any potentially inflammatory interpretations. Indeed, our aim with introducing these citation types is to highlight differences in research findings based on evidence, not opinion. The main challenge of this classification task is the highly imbalanced distribution of the three classes. Based on manual annotations of different publication domains and sources, we estimate the average distribution of citation statements as 92.6% mentioning, 6.5% supporting, and 0.8% contrasting statements. Obviously, the less frequent the class, the more valuable it is. Most of the efforts in the development of our automatic classification system have been directed to address this imbalanced distribution. This task has required first the creation of original training data by experts—scientists with experience in reading and interpreting scholarly papers. Focusing on data quality, the expert classification was realized by multiple-blind manual annotation (at least two annotators working in parallel on the same citation), followed by a reconciliation step where the disagreements were further discussed and analyzed by the annotators. To keep track of the progress of our automatic classification over time, we created a holdout set of 9,708 classified citation records. To maintain a class distribution as close as possible to the actual distribution in current scholarly publications, we extracted the citation contexts from Open Access PDF of Unpaywall by random sampling with a maximum of one context per document.

We separately developed a working set where we tried to oversample the two less frequent classes (supporting, contrasting) with the objective of addressing the difficulties implied by the imbalanced automatic classification. We exploited the classification scores of our existing classifiers to select more likely supporting and contrasting statements for manual classification. At the present time, this set contains 38,925 classified citation records. The automatic classification system was trained with this working set, and continuously evaluated with the immutable holdout set to avoid as much bias as possible. An n -fold cross-evaluation on the working set, for instance, would have been misleading because the distribution of the classes in this set was artificially modified to boost the classification accuracy of the less frequent classes.

Before reconciliation, the observed average interannotator agreement percentage was 78.5% in the open domain and close to 90% for batches in biomedicine. It is unclear what accounts for the difference. Reconciliation, further completed with expert review by core team members, resulted in highly consensual classification decisions, which contrast with typical multiround disagreement rates observed with sentiment classification. Athar (2014) , for instance, reports Cohen’s k annotator agreement of 0.675 and Ciancarini, Di Iorio et al. (2014) report k = 0.13 and k = 0.15 for the property groups covering confirm / supports and critiques citation classification labels. A custom open source document annotation web application, docanno ( Nakayama, Kubo et al., 2018 ) was deployed to support the first round of annotations.

Overall, the creation of our current training and evaluation holdout data sets has been a major 2-year effort involving up to eight expert annotators and nearly 50,000 classified citation records. In addition to the class, each record includes the citation sentence, the full “snippet” (citation sentence plus previous and next sentences), the source and target DOI, the reference callout string, and the hierarchical list of section titles where the citation occurs.

2.6. Machine Learning Classifiers

Improving the classification architecture: After initial experiments with RNN (Recursive Neural Network) architectures such as BidGRU (Bidirectional Gated Recurrent Unit, an architecture similar to the approach of Cohan et al. (2019) for citation intent classification), we obtained significant improvements with the more recently introduced ELMo (Embeddings from Language Models) dynamic embeddings ( Peters, Neumann et al., 2018 ) and an ensemble approach. Although the first experiments with BERT (Bidirectional Encoder Representations from Transformers) ( Devlin, Chang et al., 2019 ), a breakthrough architecture for NLP, were disappointing, fine-tuning SciBERT (a science-pretrained base BERT model) ( Beltagy, Lo, & Cohan, 2019 ) led to the best results and is the current production architecture of the platform.

Using oversampling and class weighting techniques: It is known that the techniques developed to address imbalanced classification in traditional machine learning can be applied successfully to deep learning too ( Johnson & Khoshgoftaar, 2019 ). We introduced in our system oversampling of less frequent classes, class weighting, and metaclassification with three binary classifiers. These techniques provide some improvements, but they rely on empirical parameters that must be re-evaluated as the training data changes.

Extending the training data for less frequent classes: As mentioned previously, we use an active learning approach to select the likely less frequent citation classes based on the scores of the existing classifiers. By focusing on edge cases over months of manual annotations, we observed significant improvements in performance for predicting contrasting and supporting cases.

Progress on classification results over approximately 1 year, evaluated on a fixed holdout set of 9,708 examples. In parallel with these various iterations on the classification algorithms, the training data was raised from 30,665 (initial evaluation with BidGRU) to 38,925 examples (last evaluation with SciBERT) via an active learning approach.

Accuracy of SciBERT classifier, currently deployed on the scite platform, evaluated on a holdout set of 9,708 examples.

Note: When deploying classification models in production, we balance the precision/recall so that all the classes have a precision higher than 80%.

Given the unique nature of scite, there are a number of additional considerations. First, scaling is a key requirement of scite, which addresses the full corpus of scientific literature. While providing good results, the prediction with the ELMo approach is 20 times slower than with SciBERT, making it less attractive for our platform. Second, we have experimented with using section titles to improve classifications—for example, one might expect to find supporting and contrasting statements more often in the Results section of a paper and mentioning statements in the Introduction. Counterintuitively, including section titles in our model had no impact on F -scores, although it did slightly improve precision. It is unclear why including section titles failed to improve F -scores. However, it might relate to the challenge of correctly identifying and normalizing section titles from documents. Third, segmenting scientific text into sentences presents unique challenges due to the prevalence of abbreviations, nomenclatures, and mathematical equations. Finally, we experimented with various context windows (i.e., the amount of text used in the classification of a citation) but were only able to improve the F -score for the contrasting category by eight points by manually selecting the most relevant phrases in the context window. Automating this process might improve classifications, but doing so presents a significant technical challenge. Other possible improvements of the classifier include multitask training, refinement of classes, increase of training data via improved active learning techniques, and integration of categorical features in the transformer classifier architecture.

We believe that the specificity of our evidence-based citation classes, the size and the focus on the quality of our manually annotated data set (multiple rounds of blind annotations with final collective reconciliation), the customization and continuous improvement of a state of the art deep learning classifier, and finally the scale of our citation analysis distinguishes our work from existing developments in automatic citation analysis.

2.7. Citation Statement and Classification Pipeline

TEI XML data is parsed in Python using the BeautifulSoup library and further segmented into sentences using a combination of spaCy ( Honnibal, Montani et al., 2018 ) and Natural Language Toolkit’s Punkt Sentence Tokenizer ( Bird, Klein, & Loper, 2009 ). These sentence segmentation candidates are then postprocessed with custom rules to better fit scientific texts, existing text structures, and inline markups. For instance, a sentence split is forbidden inside a reference callout, around common abbreviations not supported by the general-purpose sentence segmenters, or if it is conflicting with a list item, paragraph, or section break.

The implementation of the classifier is realized by a component we have named Veracity , which provides a custom set of deep learning classifiers built on top of the open source DeLFT library ( Lopez, 2020d ). Veracity is written in Python and employs Keras and TensorFlow for text classification. It runs on a single server with an NVIDIA GP102 (GeForce GTX 1080 Ti) graphics card with 3,584 CUDA cores. This single machine is capable of classifying all citation statements as they are processed. Veracity retrieves batches of text from the scite database that have yet to be classified, processes them, and updates the database with the results. When deploying classification models in production, we balance the precision/recall so that all the classes have a precision higher than 80%. For this purpose, we use the holdout data set to adjust the class weights at the prediction level. After evaluation, we can exploit all available labeled data to maximize the quality, and the holdout set captures a real-world distribution adapted to this final tuning.

2.8. User Interface

The resulting classified citations are stored and made available on the scite platform. Data from scite can be accessed in a number of ways (downloads of citations to a particular paper; the scite API, etc.). However, users will most commonly access scite through its web interface. Scite provides a number of core features, detailed below.

The scite report page ( Figure 1 ) displays summary information about a given paper. All citations in the scite database to the paper are displayed, and users can filter results by classification (supporting, mentioning, contrasting), paper section (e.g., Introduction, Results), and the type of citing article (e.g., preprint, book, etc.). Users can also search for text within citation statements and surrounding citation context. For example, if a user wishes to examine how an article has been cited with respect to a given concept (e.g., fear), they can search for citation contexts that contain that key term. Each citation statement is accompanied by a classification label, as well as an indication of how confident the model is of said classification. For example, a citation statement may be classified as supporting with 90% confidence, meaning that the model is 90% certain that the statement supports the target citation. Finally, each citation statement can be flagged by individual users as incorrect, so that users can report a classification as incorrect, as well as justify their objection. After a citation statement has been flagged as incorrect, it will be reviewed and verified by two independent reviewers, and, if both agree, the recommended change will be implemented. In this way, scite supplements machine learning with human interventions to ensure that citations are accurately classified. This is an important feature of scite that allows researchers to interact with the automated citation types, correcting classifications that might otherwise be difficult for a machine to classify. It also opens the possibility for authors and readers to add more nuance to citation typing by allowing them to annotate snippets.

To improve the utility and usability of the smart citation data, scite offers a wide variety of tools common to other citation platforms, such as Scopus and Web of Science and other information retrieval software. These include literature searching functionality for researchers to find supported and contrasted research, visualizations to see research in context, reference checking for automatically evaluating references with scite’s data on an uploaded manuscript and more. Scite also offers plugins for popular web browsers and reference management software (e.g., Zotero) that allow easy access to scite reports and data in native research environments.

3.1. Research Applications

A number of researchers have already made use of scite for quantitative assessments of the literature. For example, Bordignon (2020) examined self-correction in the scientific record and operationalized “negative” citations as those that scite classified as contrasting. They found that negative citations are rare, even among works that have been retracted. In another example from our own group, Nicholson et al. (2020) examined scientific papers cited in Wikipedia articles and found that—like the scientific literature as a whole—the vast majority presented findings that have not been subsequently verified. Similar analyses could also be applied to articles in the popular press.

One can imagine a number of additional metascientific applications. For example, network analyses with directed graphs, valenced edges (by type of citation—supporting, contrasting, and mentioning), and individual papers as nodes could aid in understanding how various fields and subfields are related. A simplified form of this analysis is already implemented on the scite website (see Figure 3 ), but more complicated analyses that assess traditional network indices, such as centrality and clustering, could be easily implemented using standard software libraries and exports of data using the scite API.

A citation network representation using the scite Visualization tool. The nodes represent individual papers, with the edges representing supporting (green) or contrasting (blue) citation statements. The graph is interactive and can be expanded and modified for other layouts. The interactive visualization can be accessed at the following link: https://scite.ai/visualizations/global-analysis-of-genome-transcriptome-9L4dJr?dois%5B0%5D=10.1038%2Fmsb.2012.40&dois%5B1%5D=10.7554%2Felife.05068&focusedElement=10.7554%2Felife.05068 .

3.2. Implications for Scholarly Publishers

There are a number of implications for scholarly publishers. At a very basic level, this is evident in the features that scite provides that are of particular use to publishers. For example, the scite Reference Check parses the reference list of an uploaded document and produces a report indicating how items in the list have been cited, flagging those that have been retracted or have otherwise been the subject of editorial concern. This type of screening can help publishers and editors ensure that articles appearing in their journals do not inadvertently cite discredited works. Evidence in scite’s own database indicates that this would solve a seemingly significant problem, as in 2019 alone nearly 6,000 published papers cited works that had been retracted prior to 2019. Given that over 95% of citations made to retracted articles are in error ( Schneider, Ye et al., 2020 ), had the Reference Check tool been applied to these papers during the review process, the majority of these mistakes could have been caught.

However, there are additional implications for scholarly publishing that go beyond the features provided by scite. We believe that by providing insights into how articles are cited—rather than simply noting that the citation has occurred—scite can alter the way in which journals, institutions, and publishers are assessed. Scite provides journals and institutions with dashboards that indicate the extent to which papers with which they are associated have been supported or contrasted by subsequent research ( Figure 4 ). Even without reliance on specific metrics, the approach that scite provides prompts the question: What if we normalized the assessment of journals, institutions and researchers in terms of how they were cited rather than the simple fact that they were cited alone?

A scite Journal Dashboard showing the aggregate citation information at the journal level, including editorial notices and the scite Index, a journal metric that shows the ratio of supporting citations over supporting plus contrasting citations. Access to the journal dashboard in the figure and other journal dashboards is available here: https://scite.ai/journals/0138-9130 .

3.3. Implications for Researchers

Given the fact that nearly 3 million scientific papers are published every year ( Ware & Mabe, 2015 ), researchers increasingly report feeling overwhelmed by the amount of literature they must sift through as part of their regular workflow ( Landhuis, 2016 ). Scite can help by assisting researchers in identifying relevant, reliable work that is narrowly tailored to their interests, as well as better understanding how a given paper fits into the broader context of the scientific literature. For example, one common technique for orienting oneself to new literature is to seek out the most highly cited papers in that area. If the context of those citations is also visible, the value of a given paper can be more completely assessed and understood. There are, however, additional—although perhaps less obvious—implications. If citation types are easily visible, it is possible that researchers will be incentivized to make replication attempts easier (for example, by providing more explicit descriptions of methods or instruments) in the hope that their work will be replicated.

3.4. Limitations

At present, the biggest limitation for researchers using scite is the size of the database. At the time of this writing, scite has ingested over 880 million separate citation statements from over 25 million scholarly publications. However, there are over 70 million scientific publications in existence ( Ware & Mabe, 2015 ); scite is constantly ingesting new papers from established sources and signing new licensing agreements with publishers, so this limitation should abate over time. However, given that the ingestion pipeline fails to identify approximately 30% of citation statements/references in PDF files (~5% in XML), the platform will necessarily contain fewer references than services such as Google Scholar and Web of Science, which do not rely on ingesting the full text of papers. Even if references are reliably extracted and matched with a DOI or directly provided by publishers, a reference is currently only visible on the scite platform if it is matched with at least one citation context in the body of the article. As such, the data provided by scite will necessarily miss a measurable percentage of citations to a given paper. We are working to address these limitations in two ways: First, we are working toward ingesting more full-text XML and improving our ability to detect document structure in PDFs. Second, we have recently supplemented our Smart Citation data with “traditional” citation metadata provided by Crossref (see “Without Citation Statements” shown in Figure 1 ), which surfaces references that we would otherwise miss. Indeed, this Crossref data now includes references from publishers with previously closed references such as Elsevier and the American Chemical Society. These traditional citations can later be augmented to include citation contexts as we gain access to full text.

Another limitation is related to the classification of citations. First, as noted previously, the Veracity software does not perfectly classify citations. This can partly be explained by the fact that language in the (biomedical) sciences is little standardized (unlike law, where shepardizing is a standing term describing the “process of using a citator to discover the history of a case or statute to determine whether it is still good law”; see Lehman & Phelps, 2005 ). However, the accuracy of the classifier will likely increase over time as technology improves and the training data set increases in size. Second, the ontology currently employed by scite (supporting, mentioning, and contrasting) necessarily misses some nuance regarding how references are cited in scientific papers. One key example relates to what “counts” as a contrasting citation: At present, this category is limited to instances where new evidence is presented (e.g., a failed replication attempt or a difference in findings). However, it might also be appropriate to include conceptual and logical arguments against a given paper in this category. Moreover, in our system, the evidence behind the supporting or contrasting citation statements is not being assessed; thus a supporting citation statement might come from a paper where the experimental evidence is weak and vice versa. We do display the citation tallies that papers have received so that users can assess this but it would be exceedingly difficult to also classify the sample size, statistics, and other parameters that define how robust a finding is.

The automated extraction and analysis of scientific citations is a technically challenging task, but one whose time has come. By surfacing the context of citations rather than relying on their mere existence as an indication of a paper’s importance and impact, scite provides a novel approach to addressing pressing questions for the scientific community, including incentivizing replicable works, assessing an increasingly large body of literature, and quantitatively studying entire scientific fields.

We would like to thank Yuri Lazebnik for his help in conceptualizing and building scite.

This work was supported by NIDA grant 4R44DA050155-02.

Josh M. Nicholson: Conceptualization, Data acquisition, Analysis and interpretation of data, Writing—original draft, Writing—Review and editing. Milo Mordaunt: Data acquisition, Analysis and interpretation of data. Patrice Lopez: Conceptualization, Analysis and interpretation of data, Writing—original draft, Writing—Review and editing. Ashish Uppala: Analysis and interpretation of data, Writing—original draft, Writing—Review and editing. Domenic Rosati: Analysis and interpretation of data, Writing—original draft, Writing—Review and editing. Neves P. Rodrigues: Conceptualization. Sean C. Rife: Conceptualization, Data acquisition, Analysis and interpretation of data, Writing—original draft, Writing—Review and editing. Peter Grabitz: Conceptualization, Data acquisition, Analysis and interpretation of data, Writing—original draft, Writing—Review and editing.

The authors are shareholders and/or consultants or employees of Scite Inc.

Code used in the ingestion of manuscripts is available at https://github.com/kermitt2/grobid , https://github.com/kermitt2/biblio-glutton , and https://github.com/kermitt2/Pub2TEI . The classification of citation statements is performed by a modified version of DeLFT ( https://github.com/kermitt2/delft ). The training data used by the scite classifier is proprietary and not publicly available. The 880+ million citation statements are available at scite.ai but cannot be shared in full due to licensing arrangements made with publishers.

Details of how retractions and other editorial notices can be detected through an automated examination of metadata—even when there is no explicit indication that such notice(s) exist—will be made public via a manuscript currently in preparation.

As an illustration, the ISTEX project has been an effort from the French state leading to the purchase of 23 million full text articles from the mainstream publishers (Elsevier, Springer-Nature, Wiley, etc.) mainly published before 2005, corresponding to an investment of €55 million in acquisitions. The delivery of full text XML when available was a contractual requirement, but an XML format with structured body could be delivered by publishers for only around 10% of the publications.

For more information on the history and prevalence of Crossref, see https://www.crossref.org/about/ .

The evaluation data and scripts are available on the project GitHub repository; see biblio-glutton ( Lopez, 2020c ).

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How technology is shaping learning in higher education

About the authors.

This article is a collaborative effort by Claudio Brasca, Charag Krishnan , Varun Marya , Katie Owen, Joshua Sirois, and Shyla Ziade, representing views from McKinsey’s Education Practice.

The COVID-19 pandemic forced a shift to remote learning overnight for most higher-education students, starting in the spring of 2020. To complement video lectures and engage students in the virtual classroom, educators adopted technologies that enabled more interactivity and hybrid models of online and in-person activities. These tools changed learning, teaching, and assessment in ways that may persist after the pandemic. Investors have taken note. Edtech start-ups raised record amounts of venture capital in 2020 and 2021, and market valuations for bigger players soared.

A study conducted by McKinsey in 2021 found that to engage most effectively with students, higher-education institutions can focus on eight dimensions  of the learning experience. In this article, we describe the findings of a study of the learning technologies that can enable aspects of several of those eight dimensions (see sidebar “Eight dimensions of the online learning experience”).

Eight dimensions of the online learning experience

Leading online higher-education institutions focus on eight key dimensions of the learning experience across three overarching principles.

Seamless journey

Clear education road map: “My online program provides a road map to achieve my life goals and helps me structure my day to day to achieve steady progress.”

Seamless connections: “I have one-click access to classes and learning resources in the virtual learning platform through my laptop or my phone.”

Engaging teaching approach

Range of learning formats: “My program offers a menu of engaging courses with both self-guided and real-time classes, and lots of interaction with instructors and peers.”

Captivating experiences: “I learn from the best professors and experts. My classes are high quality, with up-to-date content.”

Adaptive learning: “I access a personalized platform that helps me practice exercises and exams and gives immediate feedback without having to wait for the course teacher.”

Real-world skills application: “My online program helps me get hands-on practice using exciting virtual tools to solve real-world problems.”

Caring network

Timely support: “I am not alone in my learning journey and have adequate 24/7 support for academic and nonacademic issues.”

Strong community: “I feel part of an academic community and I’m able to make friends online.”

In November 2021, McKinsey surveyed 600 faculty members and 800 students from public and private nonprofit colleges and universities in the United States, including minority-serving institutions, about the use and impact of eight different classroom learning technologies (Exhibit 1). (For more on the learning technologies analyzed in this research, see sidebar “Descriptions of the eight learning technologies.”) To supplement the survey, we interviewed industry experts and higher-education professionals who make decisions about classroom technology use. We discovered which learning tools and approaches have seen the highest uptake, how students and educators view them, the barriers to higher adoption, how institutions have successfully adopted innovative technologies, and the notable impacts on learning (for details about our methodology, see sidebar “About the research”).

Double-digit growth in adoption and positive perceptions

Descriptions of the eight learning technologies.

• Classroom interactions: These are software platforms that allow students to ask questions, make comments, respond to polls, and attend breakout discussions in real time, among other features. They are downloadable and accessible from phones, computers, and tablets, relevant to all subject areas, and useful for remote and in-person learning.
• Classroom exercises: These platforms gamify learning with fun, low-stakes competitions, pose problems to solve during online classes, allow students to challenge peers to quizzes, and promote engagement with badges and awards. They are relevant to all subject areas.
• Connectivity and community building: A broad range of informal, opt-in tools, these allow students to engage with one another and instructors and participate in the learning community. They also include apps that give students 24/7 asynchronous access to lectures, expanded course materials, and notes with enhanced search and retrieval functionality.
• Group work: These tools let students collaborate in and out of class via breakout/study rooms, group preparation for exams and quizzes, and streamlined file sharing.
• Augmented reality/virtual reality (AR/VR): Interactive simulations immerse learners in course content, such as advanced lab simulations for hard sciences, medical simulations for nursing, and virtual exhibit tours for the liberal arts. AR can be offered with proprietary software on most mobile or laptop devices. VR requires special headsets, proprietary software, and adequate classroom space for simultaneous use.
• AI adaptive course delivery: Cloud-based, AI-powered software adapts course content to a student’s knowledge level and abilities. These are fully customizable by instructors and available in many subject areas, including business, humanities, and sciences.
• Machine learning–powered teaching assistants: Also known as chatbot programs, machine learning–powered teaching assistants answer student questions and explain course content outside of class. These can auto-create, deliver, and grade assignments and exams, saving instructors’ time; they are downloadable from mobile app stores and can be accessed on personal devices.
• Student progress monitoring: These tools let instructors monitor academic progress, content mastery, and engagement. Custom alerts and reports identify at-risk learners and help instructors tailor the content or their teaching style for greater effectiveness. This capability is often included with subscriptions to adaptive learning platforms.

Survey respondents reported a 19 percent average increase in overall use of these learning technologies since the start of the COVID-19 pandemic. Technologies that enable connectivity and community building, such as social media–inspired discussion platforms and virtual study groups, saw the biggest uptick in use—49 percent—followed by group work tools, which grew by 29 percent (Exhibit 2). These technologies likely fill the void left by the lack of in-person experiences more effectively than individual-focused learning tools such as augmented reality and virtual reality (AR/VR). Classroom interaction technologies such as real-time chatting, polling, and breakout room discussions were the most widely used tools before the pandemic and remain so; 67 percent of survey respondents said they currently use these tools in the classroom.

About the research

In November 2021, McKinsey surveyed 634 faculty members and 818 students from public, private, and minority-serving colleges and universities over a ten-day period. The survey included only students and faculty who had some remote- or online-learning experience with any of the eight featured technologies. Respondents were 63 percent female, 35 percent male, and 2 percent other gender identities; 69 percent White, 18 percent Black or African American, 8 percent Asian, and 4 percent other ethnicities; and represented every US region. The survey asked respondents about their:

• experiences with technology in the classroom pre-COVID-19;
• experiences with technology in the classroom since the start of the COVID-19 pandemic; and
• desire for future learning experiences in relation to technology.

The shift to more interactive and diverse learning models will likely continue. One industry expert told us, “The pandemic pushed the need for a new learning experience online. It recentered institutions to think about how they’ll teach moving forward and has brought synchronous and hybrid learning into focus.” Consequently, many US colleges and universities are actively investing to scale up their online and hybrid program offerings .

Differences in adoption by type of institution observed in the research

• Historically Black colleges and universities (HBCUs) and tribal colleges and universities made the most use of classroom interactions and group work tools (55 percent) and the least use of tools for monitoring student progress (15 percent).
• Private institutions used classroom interaction technologies (84 percent) more than public institutions (63 percent).
• Public institutions, often associated with larger student populations and course sizes, employed group work and connectivity and community-building tools more often than private institutions.
• The use of AI teaching-assistant technologies increased significantly more at public institutions (30 percent) than at private institutions (9 percent), though overall usage remained comparatively higher at private institutions.
• The use of tools for monitoring student progress increased by 14 percent at private institutions, versus no growth at public institutions.

Some technologies lag behind in adoption. Tools enabling student progress monitoring, AR/VR, machine learning–powered teaching assistants (TAs), AI adaptive course delivery, and classroom exercises are currently used by less than half of survey respondents. Anecdotal evidence suggests that technologies such as AR/VR require a substantial investment in equipment and may be difficult to use at scale in classes with high enrollment. Our survey also revealed utilization disparities based on size. Small public institutions use machine learning–powered TAs, AR/VR, and technologies for monitoring student progress at double or more the rates of medium and large public institutions, perhaps because smaller, specialized schools can make more targeted and cost-effective investments. We also found that medium and large public institutions made greater use of connectivity and community-building tools than small public institutions (57 to 59 percent compared with 45 percent, respectively). Although the uptake of AI-powered tools was slower, higher-education experts we interviewed predict their use will increase; they allow faculty to tailor courses to each student’s progress, reduce their workload, and improve student engagement at scale (see sidebar “Differences in adoption by type of institution observed in the research”).

While many colleges and universities are interested in using more technologies to support student learning, the top three barriers indicated are lack of awareness, inadequate deployment capabilities, and cost (Exhibit 3).

Students want entertaining and efficient tools

More than 60 percent of students said that all the classroom learning technologies they’ve used since COVID-19 began had improved their learning and grades (Exhibit 4). However, two technologies earned higher marks than the rest for boosting academic performance: 80 percent of students cited classroom exercises, and 71 percent cited machine learning–powered teaching assistants.

Although AR/VR is not yet widely used, 37 percent of students said they are “most excited” about its potential in the classroom. While 88 percent of students believe AR/VR will make learning more entertaining, just 5 percent said they think it will improve their ability to learn or master content (Exhibit 5). Industry experts confirmed that while there is significant enthusiasm for AR/VR, its ability to improve learning outcomes is uncertain. Some data look promising. For example, in a recent pilot study, 1 “Immersive biology in the Alien Zoo: A Dreamscape Learn software product,” Dreamscape Learn, accessed October 2021. students who used a VR tool to complete coursework for an introductory biology class improved their subject mastery by an average of two letter grades.

Faculty embrace new tools but would benefit from more technical support and training

Faculty gave learning tools even higher marks than students did, for ease of use, engagement, access to course resources, and instructor connectivity. They also expressed greater excitement than students did for the future use of technologies. For example, while more than 30 percent of students expressed excitement for AR/VR and classroom interactions, more than 60 percent of faculty were excited about those, as well as machine learning–powered teaching assistants and AI adaptive technology.

Eighty-one percent or more of faculty said they feel the eight learning technology tools are a good investment of time and effort relative to the value they provide (Exhibit 6). Expert interviews suggest that employing learning technologies can be a strain on faculty members, but those we surveyed said this strain is worthwhile.

While faculty surveyed were enthusiastic about new technologies, experts we interviewed stressed some underlying challenges. For example, digital-literacy gaps have been more pronounced since the pandemic because it forced the near-universal adoption of some technology solutions, deepening a divide that was unnoticed when adoption was sporadic. More tech-savvy instructors are comfortable with interaction-engagement-focused solutions, while staff who are less familiar with these tools prefer content display and delivery-focused technologies.

According to experts we interviewed, learning new tools and features can bring on general fatigue. An associate vice president of e-learning at one university told us that faculty there found designing and executing a pilot study of VR for a computer science class difficult. “It’s a completely new way of instruction. . . . I imagine that the faculty using it now will not use it again in the spring.” Technical support and training help. A chief academic officer of e-learning who oversaw the introduction of virtual simulations for nursing and radiography students said that faculty holdouts were permitted to opt out but not to delay the program. “We structured it in a ‘we’re doing this together’ way. People who didn’t want to do it left, but we got a lot of support from vendors and training, which made it easy to implement simulations.”

Reimagining higher education in the United States

Takeaways from our research.

Despite the growing pains of digitizing the classroom learning experience, faculty and students believe there is a lot more they can gain. Faculty members are optimistic about the benefits, and students expect learning to stay entertaining and efficient. While adoption levels saw double-digit growth during the pandemic, many classrooms have yet to experience all the technologies. For institutions considering the investment, or those that have already started, there are several takeaways to keep in mind.

• It’s important for administration leaders, IT, and faculty to agree on what they want to accomplish by using a particular learning technology. Case studies and expert interviews suggest institutions that seek alignment from all their stakeholders before implementing new technologies are more successful. Is the primary objective student engagement and motivation? Better academic performance? Faculty satisfaction and retention? Once objectives are set, IT staff and faculty can collaborate more effectively in choosing the best technology and initiating programs.
• Factor in student access to technology before deployment. As education technology use grows, the digital divide for students puts access to education at risk. While all the institution types we surveyed use learning technologies in the classroom, they do so to varying degrees. For example, 55 percent of respondents from historically Black colleges and universities and tribal colleges and universities use classroom interaction tools. This is lower than public institutions’ overall utilization rate of 64 percent and private institutions’ utilization rate of 84 percent. Similarly, 15 percent of respondents from historically Black colleges and universities and tribal colleges and universities use tools for monitoring student progress, while the overall utilization rate for both public and private institutions is 25 percent.
• High-quality support eases adoption for students and faculty. Institutions that have successfully deployed new learning technologies provided technical support and training for students and guidance for faculty on how to adapt their course content and delivery. For example, institutions could include self-service resources, standardize tools for adoption, or provide stipend opportunities for faculty who attend technical training courses. One chief academic officer told us, “The adoption of platforms at the individual faculty level can be very difficult. Ease of use is still very dependent upon your IT support representative and how they will go to bat to support you.”
• Agree on impact metrics and start measuring in advance of deployment. Higher-education institutions often don’t have the means to measure the impact of their investment in learning technologies, yet it’s essential for maximizing returns. Attributing student outcomes to a specific technology can be complex due to the number of variables involved in academic performance. However, prior to investing in learning technologies, the institution and its faculty members can align on a core set of metrics to quantify and measure their impact. One approach is to measure a broad set of success indicators, such as tool usage, user satisfaction, letter grades, and DFW rates (the percentage of students who receive a D, F, or Withdraw) each term. The success indicators can then be correlated by modality—online versus hybrid versus in-class—to determine the impact of specific tools. Some universities have offered faculty grants of up to $20,000 for running pilot programs that assess whether tools are achieving high-priority objectives. “If implemented properly, at the right place, and with the right buy-in, education technology solutions are absolutely valuable and have a clear ROI,” a senior vice president of academic affairs and chief technology officer told us.

In an earlier article , we looked at the broader changes in higher education that have been prompted by the pandemic. But perhaps none has advanced as quickly as the adoption of digital learning tools. Faculty and students see substantial benefits, and adoption rates are a long way from saturation, so we can expect uptake to continue. Institutions that want to know how they stand in learning tech adoption can measure their rates and benchmark them against the averages in this article and use those comparisons to help them decide where they want to catch up or get ahead.

Claudio Brasca is a partner in McKinsey’s Bay Area office, where Varun Marya is a senior partner; Charag Krishnan is a partner in the New Jersey office; Katie Owen is an associate partner in the St. Louis office, where Joshua Sirois is a consultant; and Shyla Ziade is a consultant in the Denver office.

The authors wish to thank Paul Kim, chief technology officer and associate dean at Stanford School of Education, and Ryan Golden for their contributions to this article.

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