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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 167: International Conference on Algorithmic Learning Theory, 29-1 April 2022, Paris, France

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Editors: Sanjoy Dasgupta, Nika Haghtalab

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Filter Authors: Filter Titles:

Algorithmic Learning Theory 2022: Preface

; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:1-2

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Efficient Methods for Online Multiclass Logistic Regression

Naman Agarwal, Satyen Kale, Julian Zimmert; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:3-33

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Understanding Simultaneous Train and Test Robustness

Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:34-69

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Learning what to remember

Robi Bhattacharjee, Gaurav Mahajan; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:70-89

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Learning with Distributional Inverters

Eric Binnendyk, Marco Carmosino, Antonina Kolokolova, R Ramyaa, Manuel Sabin; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:90-106

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Universal Online Learning with Unbounded Losses: Memory Is All You Need

Moïse Blanchard, Romain Cosson, Steve Hanneke; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:107-127

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Social Learning in Non-Stationary Environments

Etienne Boursier, Vianney Perchet, Marco Scarsini; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:128-129

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Iterated Vector Fields and Conservatism, with Applications to Federated Learning

Zachary Charles, Keith Rush; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:130-147

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Implicit Parameter-free Online Learning with Truncated Linear Models

Keyi Chen, Ashok Cutkosky, Francesco Orabona; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:148-175

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Faster Perturbed Stochastic Gradient Methods for Finding Local Minima

Zixiang Chen, Dongruo Zhou, Quanquan Gu; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:176-204

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Algorithms for learning a mixture of linear classifiers

Aidao Chen, Anindya De, Aravindan Vijayaraghavan; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:205-226

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Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games

Zixiang Chen, Dongruo Zhou, Quanquan Gu; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:227-261

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Refined Lower Bounds for Nearest Neighbor Condensation

Rajesh Chitnis; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:262-281

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Leveraging Initial Hints for Free in Stochastic Linear Bandits

Ashok Cutkosky, Chris Dann, Abhimanyu Das, Qiuyi Zhang; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:282-318

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Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians

Sami Davies, Arya Mazumdar, Soumyabrata Pal, Cyrus Rashtchian; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:319-341

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Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature

Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:342-380

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Privacy Amplification via Shuffling for Linear Contextual Bandits

Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:381-407

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Multicalibrated Partitions for Importance Weights

Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:408-435

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Efficient and Optimal Fixed-Time Regret with Two Experts

Laura Greenstreet, Nicholas J. A. Harvey, Victor Sanches Portella; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:436-464

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Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems

Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:465-487

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Universally Consistent Online Learning with Arbitrarily Dependent Responses

Steve Hanneke; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:488-497

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Distinguishing Relational Pattern Languages With a Small Number of Short Strings

Robert C. Holte, S. Mahmoud Mousawi, Sandra Zilles; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:498-514

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Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability

Lunjia Hu, Charlotte Peale, Omer Reingold; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:515-552

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Adversarial Interpretation of Bayesian Inference

Hisham Husain, Jeremias Knoblauch; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:553-572

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Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure

Hsu Kao, Chen-Yu Wei, Vijay Subramanian; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:573-605

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Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems

Belhal Karimi, Hoi-To Wai, Eric Moulines, Ping Li; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:606-637

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Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally

Pravesh K. Kothari, Peter Manohar, Brian Hu Zhang; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:638-667

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Improved rates for prediction and identification of partially observed linear dynamical systems

Holden Lee; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:668-698

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On the Last Iterate Convergence of Momentum Methods

Xiaoyu Li, Mingrui Liu, Francesco Orabona; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:699-717

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The Mirror Langevin Algorithm Converges with Vanishing Bias

Ruilin Li, Molei Tao, Santosh S. Vempala, Andre Wibisono; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:718-742

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On the Initialization for Convex-Concave Min-max Problems

Mingrui Liu, Francesco Orabona; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:743-767

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Global Riemannian Acceleration in Hyperbolic and Spherical Spaces

David Martínez-Rubio; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:768-826

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Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets

Depen Morwani, Harish G. Ramaswamy; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:827-880

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Infinitely Divisible Noise in the Low Privacy Regime

Rasmus Pagh, Nina Mesing Stausholm; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:881-909

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Scale-Free Adversarial Multi Armed Bandits

Sudeep Raja Putta, Shipra Agrawal; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:910-930

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Asymptotic Degradation of Linear Regression Estimates with Strategic Data Sources

Benjamin Roussillon, Nicolas Gast, Patrick Loiseau, Panayotis Mertikopoulos; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:931-967

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Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability

Aadirupa Saha, Akshay Krishnamurthy; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:968-994

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Faster Rates of Private Stochastic Convex Optimization

Jinyan Su, Lijie Hu, Di Wang; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:995-1002

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Distributed Online Learning for Joint Regret with Communication Constraints

Dirk Van der Hoeven, Hédi Hadiji, Tim van Erven; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:1003-1042

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A Model Selection Approach for Corruption Robust Reinforcement Learning

Chen-Yu Wei, Christoph Dann, Julian Zimmert; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:1043-1096

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TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions

Gellért Weisz, Csaba Szepesvári, András György; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:1097-1137

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Faster Noisy Power Method

Zhiqiang Xu, Ping Li; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:1138-1164

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Efficient local planning with linear function approximation

Dong Yin, Botao Hao, Yasin Abbasi-Yadkori, Nevena Lazić, Csaba Szepesvári; Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:1165-1192

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