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Volume 272: Algorithmic Learning Theory, 24-27 February 2025, Politecnico di Milano, Milan, Italy

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Editors: Gautam Kamath, Po-Ling Loh

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Generalization bounds for mixing processes via delayed online-to-PAC conversions

Baptiste Abélès, Eugenio Clerico, Gergely Neu; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:23-40

Algorithmic Learning Theory 2025: Preface

Gautam Kamath, Po-Ling Loh; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1-3

When and why randomised exploration works (in linear bandits)

Marc Abeille, David Janz, Ciara Pike-Burke; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:4-22

Agnostic Private Density Estimation for GMMs via List Global Stability

Mohammad Afzali, Hassan Ashtiani, Christopher Liaw; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:41-66

Refining the Sample Complexity of Comparative Learning

Sajad Ashkezari, Ruth Urner; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:67-88

Understanding Aggregations of Proper Learners in Multiclass Classification

Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:89-111

Proper Learnability and the Role of Unlabeled Data

Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:112-133

Sample Compression Scheme Reductions

Idan Attias, Steve Hanneke, Arvind Ramaswami; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:134-162

Strategyproof Learning with Advice

Eric Balkanski, Cherlin Zhu; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:163-166

Cost-Free Fairness in Online Correlation Clustering

Eric Balkanski, Jason Chatzitheodorou, Andreas Maggiori; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:167-203

Non-stochastic Bandits With Evolving Observations

Yogev Bar-On, Yishay Mansour; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:204-227

Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem

Avrim Blum, Kavya Ravichandran; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:228-245

A Model for Combinatorial Dictionary Learning and Inference

Avrim Blum, Kavya Ravichandran; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:246-288

Differentially Private Multi-Sampling from Distributions

Albert Cheu, Debanuj Nayak; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:289-314

Near-Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches

Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Guha Thakurta; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:315-348

Generalisation under gradient descent via deterministic PAC-Bayes

Eugenio Clerico, Tyler Farghly, George Deligiannidis, Benjamin Guedj, Arnaud Doucet; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:349-389

Boosting, Voting Classifiers and Randomized Sample Compression Schemes

Arthur da Cunha, Kasper Green Larsen, Martin Ritzert; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:390-404

Effective Littlestone dimension

Valentino Delle Rose, Alexander Kozachinskiy, Tomasz Steifer; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:405-417

Is Transductive Learning Equivalent to PAC Learning?

Shaddin Dughmi, Yusuf Hakan Kalayci, Grayson York; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:418-443

Full Swap Regret and Discretized Calibration

Maxwell Fishelson, Robert Kleinberg, Princewill Okoroafor, Renato Paes Leme, Jon Schneider, Yifeng Teng; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:444-480

A PAC-Bayesian Link Between Generalisation and Flat Minima

Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:481-511

Reliable Active Apprenticeship Learning

Steve Hanneke, Liu Yang, Gongju Wang, Yulun Song; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:512-538

For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent

Steve Hanneke, Amirreza Shaeiri, Hongao Wang; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:539-559

A Complete Characterization of Learnability for Stochastic Noisy Bandits

Steve Hanneke, Kun Wang; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:560-577

Efficient Optimal PAC Learning

Mikael Høgsgaard Møller; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:578-580

Do PAC-Learners Learn the Marginal Distribution?

Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:581-610

Optimal and learned algorithms for the online list update problem with Zipfian accesses

Piotr Indyk, Isabelle Quaye, Ronitt Rubinfeld, Sandeep Silwal; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:611-648

Information-Theoretic Guarantees for Recovering Low-Rank Tensors from Symmetric Rank-One Measurements

Eren C. Kızıldağ; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:649-652

Sharp bounds on aggregate expert error

Aryeh Kontorovich, Ariel Avital; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:653-663

Quantile Multi-Armed Bandits with 1-bit Feedback

Ivan Lau, Jonathan Scarlett; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:664-699

On the Hardness of Learning One Hidden Layer Neural Networks

Shuchen Li, Ilias Zadik, Manolis Zampetakis; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:700-701

Minimax-optimal and Locally-adaptive Online Nonparametric Regression

Paul Liautaud, Pierre Gaillard, Olivier Wintenberger; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:702-735

Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression

Jackie Lok, Rishi Sonthalia, Elizaveta Rebrova; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:736-770

Computationally efficient reductions between some statistical models

Mengqi Lou, Guy Bresler, Ashwin Pananjady; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:771-771

Enhanced H-Consistency Bounds

Anqi Mao, Mehryar Mohri, Yutao Zhong; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:772-813

Center-Based Approximation of a Drifting Distribution

Alessio Mazzetto, Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:814-845

Fast Convergence of Φ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler

Siddharth Mitra, Andre Wibisono; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:846-869

A Characterization of List Regression

Chirag Pabbaraju, Sahasrajit Sarmasarkar; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:870-920

On Generalization Bounds for Neural Networks with Low Rank Layers

Andrea Pinto, Akshay Rangamani, Tomaso A Poggio; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:921-936

Data Dependent Regret Bounds for Online Portfolio Selection with Predicted Returns

Sudeep Raja Putta, Shipra Agrawal; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:937-984

A Unified Theory of Supervised Online Learnability

Vinod Raman, Unique Subedi, Ambuj Tewari; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:985-1007

An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems.

Sarah Sachs, Hedi Hadiji, Tim Van Erven, Mathias Staudigl; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1008-1040

The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization

Matan Schliserman, Uri Sherman, Tomer Koren; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1041-1107

Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate

Jie Shen; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1108-1137

Self-Directed Node Classification on Graphs

Georgy Sokolov, Maximilian Thiessen, Margarita Akhmejanova, Fabio Vitale, Francesco Orabona; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1138-1168

High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm

Vishwak Srinivasan, Andre Wibisono, Ashia Wilson; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1169-1220

Clustering with bandit feedback: breaking down the computation/information gap

Victor Thuot, Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1221-1284

Online Learning of Quantum States with Logarithmic Loss via VB-FTRL

Wei-Fu Tseng, Kai-Chun Chen, Zi-Hong Xiao, Yen-Huan Li; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1285-1312

Noisy Computing of the Threshold Function

Ziao Wang, Nadim Ghaddar, Banghua Zhu, Lele Wang; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1313-1315

How rotation invariant algorithms are fooled by noise on sparse targets

Manfred K. Warmuth, Wojciech Kot\polishlowski, Matt Jones, Ehsan Amid; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1316-1360

Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit

Julien Zhou, Pierre Gaillard, Thibaud Rahier, Julyan Arbel; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1361-1385

The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis

Matthew Zurek, Yudong Chen; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1386-1387

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