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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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Volume 117: Algorithmic Learning Theory, , San Diego, California, USA

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Editors: Aryeh Kontorovich, Gergely Neu

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Algorithmic Learning Theory 2020: Preface

; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:1-2

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Optimal multiclass overfitting by sequence reconstruction from Hamming queries

Jayadev Acharya, Ananda Theertha Suresh; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:3-21

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Leverage Score Sampling for Faster Accelerated Regression and ERM

Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin-Tat Lee, Praneeth Netrapalli, Aaron Sidford; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:22-47

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On Learnability wih Computable Learners

Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:48-60

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Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions

Shubhada Agrawal, Sandeep Juneja, Peter Glynn; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:61-110

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A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates

Yossi Arjevani, Ohad Shamir, Nathan Srebro; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:111-132

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Distribution Free Learning with Local Queries

Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:133-147

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Robust Algorithms for Online $k$-means Clustering

Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:148-173

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What relations are reliably embeddable in Euclidean space?

Robi Bhattacharjee, Sanjoy Dasgupta; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:174-195

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First-Order Bayesian Regret Analysis of Thompson Sampling

Sébastien Bubeck, Mark Sellke; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:196-233

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Cooperative Online Learning: Keeping your Neighbors Updated

Nicolò Cesa-Bianchi, Tommaso Cesari, Claire Monteleoni; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:234-250

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Cautious Limit Learning

Vanja Doskoč, Timo Kötzing; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:251-276

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Interactive Learning of a Dynamic Structure

Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian, Robert E. Schapire; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:277-296

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Sampling Without Compromising Accuracy in Adaptive Data Analysis

Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:297-318

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An adaptive stochastic optimization algorithm for resource allocation

Xavier Fontaine, Shie Mannor, Vianney Perchet; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:319-363

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Adversarially Robust Learning Could Leverage Computational Hardness.

Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mahmoody Mohammad; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:364-385

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Exponentiated Gradient Meets Gradient Descent

Udaya Ghai, Elad Hazan, Yoram Singer; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:386-407

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The Nonstochastic Control Problem

Elad Hazan, Sham Kakade, Karan Singh; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:408-421

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On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes

Kothari K. Pravesh, Livni Roi; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:422-450

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Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop

Dmitry Kovalev, Samuel Horváth, Peter Richtárik; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:451-467

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Algebraic and Analytic Approaches for Parameter Learning in Mixture Models

Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:468-489

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Robust guarantees for learning an autoregressive filter

Holden Lee, Cyril Zhang; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:490-517

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Thompson Sampling for Adversarial Bit Prediction

Yuval Lewi, Haim Kaplan, Yishay Mansour; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:518-553

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On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption

Hanti Lin, Jiji Zhang; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:554-582

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On the Complexity of Proper Distribution-Free Learning of Linear Classifiers

Philip M. Long, Raphael J. Long; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:583-591

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Feedback graph regret bounds for Thompson Sampling and UCB

Thodoris Lykouris, Éva Tardos, Drishti Wali; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:592-614

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Toward universal testing of dynamic network models

Abram Magner, Wojciech Szpankowski; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:615-633

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On the Analysis of EM for truncated mixtures of two Gaussians

Sai Ganesh Nagarajan, Ioannis Panageas; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:634-659

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A Non-Trivial Algorithm Enumerating Relevant Features over Finite Fields

Mikito Nanashima; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:660-686

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Privately Answering Classification Queries in the Agnostic PAC Model

Anupama Nandi, Raef Bassily; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:687-703

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Efficient Private Algorithms for Learning Large-Margin Halfspaces

Huy Lê Nguyễn, Jonathan Ullman, Lydia Zakynthinou; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:704-724

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Finding Robust Nash equilibria

Vianney Perchet; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:725-751

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Top-$k$ Combinatorial Bandits with Full-Bandit Feedback

Idan Rejwan, Yishay Mansour; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:752-776

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Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions

Charles Riou, Junya Honda; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:777-826

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Approximate Representer Theorems in Non-reflexive Banach Spaces

Kevin Schlegel; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:827-844

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Online Non-Convex Learning: Following the Perturbed Leader is Optimal

Arun Sai Suggala, Praneeth Netrapalli; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:845-861

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Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling

Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:862-889

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Mixing Time Estimation in Ergodic Markov Chains from a Single Trajectory with Contraction Methods

Geoffrey Wolfer; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:890-905

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Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies

Tom Zahavy, Avinatan Hasidim, Haim Kaplan, Yishay Mansour; Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:906-934

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