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| 2024 | Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies. Shlomi Weitzman, Sivan Sabato |
| 2024 | Adversarial Contextual Bandits Go Kernelized. Gergely Neu, Julia Olkhovskaya, Sattar Vakili |
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| 2024 | Agnostic Membership Query Learning with Nontrivial Savings: New Results and Techniques. Ari Karchmer |
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| 2024 | Computation with Sequences of Assemblies in a Model of the Brain. Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala |
| 2024 | Concentration of empirical barycenters in metric spaces. Victor-Emmanuel Brunel, Jordan Serres |
| 2024 | Corruption-Robust Lipschitz Contextual Search. Shiliang Zuo |
| 2024 | Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates. Michael Menart, Enayat Ullah, Raman Arora, Raef Bassily, Cristóbal Guzmán |
| 2024 | Distances for Markov Chains, and Their Differentiation. Tristan Brugère, Zhengchao Wan, Yusu Wang |
| 2024 | Dueling Optimization with a Monotone Adversary. Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, Yuanyuan Yang |
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| 2024 | Importance-Weighted Offline Learning Done Right. Germano Gabbianelli, Gergely Neu, Matteo Papini |
| 2024 | Improving Adaptive Online Learning Using Refined Discretization. Zhiyu Zhang, Heng Yang, Ashok Cutkosky, Ioannis Ch. Paschalidis |
| 2024 | International Conference on Algorithmic Learning Theory, 25-28 February 2024, La Jolla, California, USA. Claire Vernade, Daniel Hsu |
| 2024 | Learning Hypertrees From Shortest Path Queries. Shaun M. Fallat, Valerii Maliuk, Seyed Ahmad Mojallal, Sandra Zilles |
| 2024 | Learning Spanning Forests Optimally in Weighted Undirected Graphs with CUT queries. Hang Liao, Deeparnab Chakrabarty |
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| 2024 | Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. Mohammad Afzali, Hassan Ashtiani, Christopher Liaw |
| 2024 | Multiclass Learnability Does Not Imply Sample Compression. Chirag Pabbaraju |
| 2024 | Multiclass Online Learnability under Bandit Feedback. Ananth Raman, Vinod Raman, Unique Subedi, Idan Mehalel, Ambuj Tewari |
| 2024 | Near-continuous time Reinforcement Learning for continuous state-action spaces. Lorenzo Croissant, Marc Abeille, Bruno Bouchard |
| 2024 | Not All Learnable Distribution Classes are Privately Learnable. Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal |
| 2024 | On the Computational Benefit of Multimodal Learning. Zhou Lu |
| 2024 | On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. Amit Daniely, Elad Granot |
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| 2024 | Online Recommendations for Agents with Discounted Adaptive Preferences. William Brown, Arpit Agarwal |
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| 2024 | Partially Interpretable Models with Guarantees on Coverage and Accuracy. Nave Frost, Zachary C. Lipton, Yishay Mansour, Michal Moshkovitz |
| 2024 | Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2024 | Preface. |
| 2024 | Private PAC Learning May be Harder than Online Learning. Mark Bun, Aloni Cohen, Rathin Desai |
| 2024 | Provable Accelerated Convergence of Nesterov's Momentum for Deep ReLU Neural Networks. Fangshuo Liao, Anastasios Kyrillidis |
| 2024 | RedEx: Beyond Fixed Representation Methods via Convex Optimization. Amit Daniely, Mariano Schain, Gilad Yehudai |
| 2024 | Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. Pranjal Awasthi, Satyen Kale, Ankit Pensia |
| 2024 | Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs. Ian A. Kash, Lev Reyzin, Zishun Yu |
| 2024 | The Attractor of the Replicator Dynamic in Zero-Sum Games. Oliver Biggar, Iman Shames |
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