| 2025 | A Characterization of List Regression. Chirag Pabbaraju, Sahasrajit Sarmasarkar |
| 2025 | A Complete Characterization of Learnability for Stochastic Noisy Bandits. Steve Hanneke, Kun Wang |
| 2025 | A Model for Combinatorial Dictionary Learning and Inference. Avrim Blum, Kavya Ravichandran |
| 2025 | A PAC-Bayesian Link Between Generalisation and Flat Minima. Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj |
| 2025 | A Unified Theory of Supervised Online Learnability. Vinod Raman, Unique Subedi, Ambuj Tewari |
| 2025 | Agnostic Private Density Estimation for GMMs via List Global Stability. Mohammad Afzali, Hassan Ashtiani, Christopher Liaw |
| 2025 | An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems. Sarah Sachs, Hédi Hadiji, Tim van Erven, Mathias Staudigl |
| 2025 | Boosting, Voting Classifiers and Randomized Sample Compression Schemes. Arthur da Cunha, Kasper Green Larsen, Martin Ritzert |
| 2025 | Center-Based Approximation of a Drifting Distribution. Alessio Mazzetto, Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal |
| 2025 | Clustering with bandit feedback: breaking down the computation/information gap. Victor Thuot, Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen |
| 2025 | Computationally efficient reductions between some statistical models. Mengqi Lou, Guy Bresler, Ashwin Pananjady |
| 2025 | Cost-Free Fairness in Online Correlation Clustering. Eric Balkanski, Jason Chatzitheodorou, Andreas Maggiori |
| 2025 | Data Dependent Regret Bounds for Online Portfolio Selection with Predicted Returns. Sudeep Raja Putta, Shipra Agrawal |
| 2025 | Differentially Private Multi-Sampling from Distributions. Albert Cheu, Debanuj Nayak |
| 2025 | Do PAC-Learners Learn the Marginal Distribution? Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan |
| 2025 | Effective Littlestone dimension. Valentino Delle Rose, Alexander Kozachinskiy, Tomasz Steifer |
| 2025 | Efficient Optimal PAC Learning. Mikael Møller Høgsgaard |
| 2025 | Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate. Jie Shen |
| 2025 | Enhanced H-Consistency Bounds. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2025 | Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression. Jackie Lok, Rishi Sonthalia, Elizaveta Rebrova |
| 2025 | Fast Convergence of Φ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler. Siddharth Mitra, Andre Wibisono |
| 2025 | For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent. Steve Hanneke, Amirreza Shaeiri, Hongao Wang |
| 2025 | Full Swap Regret and Discretized Calibration. Maxwell Fishelson, Robert Kleinberg, Princewill Okoroafor, Renato Paes Leme, Jon Schneider, Yifeng Teng |
| 2025 | Generalisation under gradient descent via deterministic PAC-Bayes. Eugenio Clerico, Tyler Farghly, George Deligiannidis, Benjamin Guedj, Arnaud Doucet |
| 2025 | Generalization bounds for mixing processes via delayed online-to-PAC conversions. Baptiste Abélès, Eugenio Clerico, Gergely Neu |
| 2025 | High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm. Vishwak Srinivasan, Andre Wibisono, Ashia Wilson |
| 2025 | How rotation invariant algorithms are fooled by noise on sparse targets. Manfred K. Warmuth, Wojciech Kotlowski, Matt Jones, Ehsan Amid |
| 2025 | Information-Theoretic Guarantees for Recovering Low-Rank Tensors from Symmetric Rank-One Measurements. Eren C. Kizildag |
| 2025 | International Conference on Algorithmic Learning Theory, 24-27 February 2025, Politecnico di Milano, Milan, Italy. Gautam Kamath, Po-Ling Loh |
| 2025 | Is Transductive Learning Equivalent to PAC Learning? Shaddin Dughmi, Yusuf Hakan Kalayci, Grayson York |
| 2025 | Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit. Julien Zhou, Pierre Gaillard, Thibaud Rahier, Julyan Arbel |
| 2025 | Minimax-optimal and Locally-adaptive Online Nonparametric Regression. Paul Liautaud, Pierre Gaillard, Olivier Wintenberger |
| 2025 | 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 |
| 2025 | Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem. Avrim Blum, Kavya Ravichandran |
| 2025 | Noisy Computing of the Threshold Function. Ziao Wang, Nadim Ghaddar, Banghua Zhu, Lele Wang |
| 2025 | Non-stochastic Bandits With Evolving Observations. Yogev Bar-On, Yishay Mansour |
| 2025 | On Generalization Bounds for Neural Networks with Low Rank Layers. Andrea Pinto, Akshay Rangamani, Tomaso A. Poggio |
| 2025 | On the Hardness of Learning One Hidden Layer Neural Networks. Shuchen Li, Ilias Zadik, Manolis Zampetakis |
| 2025 | Online Learning of Quantum States with Logarithmic Loss via VB-FTRL. Wei-Fu Tseng, Kai-Chun Chen, Zi-Hong Xiao, Yen-Huan Li |
| 2025 | Optimal and learned algorithms for the online list update problem with Zipfian accesses. Piotr Indyk, Isabelle Quaye, Ronitt Rubinfeld, Sandeep Silwal |
| 2025 | Preface. |
| 2025 | Proper Learnability and the Role of Unlabeled Data. Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng |
| 2025 | Quantile Multi-Armed Bandits with 1-bit Feedback. Ivan Lau, Jonathan Scarlett |
| 2025 | Refining the Sample Complexity of Comparative Learning. Sajad Ashkezari, Ruth Urner |
| 2025 | Reliable Active Apprenticeship Learning. Steve Hanneke, Liu Yang, Gongju Wang, Yulun Song |
| 2025 | Sample Compression Scheme Reductions. Idan Attias, Steve Hanneke, Arvind Ramaswami |
| 2025 | Self-Directed Node Classification on Graphs. Georgy Sokolov, Maximilian Thiessen, Margarita Akhmejanova, Fabio Vitale, Francesco Orabona |
| 2025 | Sharp bounds on aggregate expert error. Aryeh Kontorovich, Ariel Avital |
| 2025 | Strategyproof Learning with Advice. Eric Balkanski, Cherlin Zhu |
| 2025 | The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization. Matan Schliserman, Uri Sherman, Tomer Koren |
| 2025 | The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis. Matthew Zurek, Yudong Chen |
| 2025 | Understanding Aggregations of Proper Learners in Multiclass Classification. Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas |
| 2025 | When and why randomised exploration works (in linear bandits). Marc Abeille, David Janz, Ciara Pike-Burke |