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Editors: Gautam Kamath, Po-Ling Loh
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Algorithmic Learning Theory 2025: Preface
; Proceedings of The 36th International Conference on Algorithmic Learning Theory, PMLR 272:1-3
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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
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
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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