| 2024 | Importance-Weighted Offline Learning Done Right. | Germano Gabbianelli, Gergely Neu, Matteo Papini |
| 2024 | Partially Interpretable Models with Guarantees on Coverage and Accuracy. | Nave Frost, Zachary C. Lipton, Yishay Mansour, Michal Moshkovitz |
| 2024 | Learning Hypertrees From Shortest Path Queries. | Shaun M. Fallat, Valerii Maliuk, Seyed Ahmad Mojallal, Sandra Zilles |
| 2024 | The Dimension of Self-Directed Learning. | Pramith Devulapalli, Steve Hanneke |
| 2024 | RedEx: Beyond Fixed Representation Methods via Convex Optimization. | Amit Daniely, Mariano Schain, Gilad Yehudai |
| 2024 | On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. | Amit Daniely, Elad Granot |
| 2024 | Computation with Sequences of Assemblies in a Model of the Brain. | Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala |
| 2024 | Near-continuous time Reinforcement Learning for continuous state-action spaces. | Lorenzo Croissant, Marc Abeille, Bruno Bouchard |
| 2024 | Learning bounded-degree polytrees with known skeleton. | Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya, Clment L. Canonne |
| 2024 | Private PAC Learning May be Harder than Online Learning. | Mark Bun, Aloni Cohen, Rathin Desai |
| 2024 | Not All Learnable Distribution Classes are Privately Learnable. | Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal |
| 2024 | Concentration of empirical barycenters in metric spaces. | Victor-Emmanuel Brunel, Jordan Serres |
| 2024 | Distances for Markov Chains, and Their Differentiation. | Tristan Brugre, Zhengchao Wan, Yusu Wang |
| 2024 | Online Recommendations for Agents with Discounted Adaptive Preferences. | William Brown, Arpit Agarwal |
| 2024 | Dueling Optimization with a Monotone Adversary. | Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, Yuanyuan Yang |
| 2024 | Tight Bounds for Local Glivenko-Cantelli. | Mose Blanchard, Vclav Vorcek |
| 2024 | The Attractor of the Replicator Dynamic in Zero-Sum Games. | Oliver Biggar, Iman Shames |
| 2024 | Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. | Pranjal Awasthi, Satyen Kale, Ankit Pensia |
| 2024 | CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. | Shubhada Agrawal, Timothe Mathieu, Debabrota Basu, Odalric-Ambrym Maillard |
| 2024 | Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. | Mohammad Afzali, Hassan Ashtiani, Christopher Liaw |
| 2024 | A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. | Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth |
| 2024 | Learning Spanning Forests Optimally in Weighted Undirected Graphs with CUT queries. | Hang Liao, Deeparnab Chakrabarty |
| 2023 | Universal Bias Reduction in Estimation of Smooth Additive Function in High Dimensions. | Fan Zhou, Ping Li, Cun-Hui Zhang |
| 2023 | Algorithmic Learning Theory 2023: Preface. | |
| 2023 | Best-of-Both-Worlds Algorithms for Partial Monitoring. | Taira Tsuchiya, Shinji Ito, Junya Honda |