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ORCID35th ALT 2024: La Jolla, CA, USA
- Claire Vernade, Daniel Hsu:
International Conference on Algorithmic Learning Theory, 25-28 February 2024, La Jolla, California, USA. Proceedings of Machine Learning Research 237, PMLR 2024 - Preface. 1-2
- Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth:
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. 3-46 - Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. 47-73 - Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard:
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. 74-124 - Pranjal Awasthi, Satyen Kale, Ankit Pensia:
Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. 125-160 - Oliver Biggar, Iman Shames:
The Attractor of the Replicator Dynamic in Zero-Sum Games. 161-178 - Moïse Blanchard, Václav Vorácek:
Tight Bounds for Local Glivenko-Cantelli. 179-220 - Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj
, Aadirupa Saha, Yuanyuan Yang:
Dueling Optimization with a Monotone Adversary. 221-243 - William Brown, Arpit Agarwal:
Online Recommendations for Agents with Discounted Adaptive Preferences. 244-281 - Tristan Brugère, Zhengchao Wan, Yusu Wang:
Distances for Markov Chains, and Their Differentiation. 282-336 - Victor-Emmanuel Brunel, Jordan Serres:
Concentration of empirical barycenters in metric spaces. 337-361 - Mark Bun, Aloni Cohen, Rathin Desai:
Private PAC Learning May be Harder than Online Learning. 362-389 - Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal:
Not All Learnable Distribution Classes are Privately Learnable. 390-401 - Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya, Clément L. Canonne:
Learning bounded-degree polytrees with known skeleton. 402-443 - Lorenzo Croissant, Marc Abeille, Bruno Bouchard:
Near-continuous time Reinforcement Learning for continuous state-action spaces. 444-498 - Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala:
Computation with Sequences of Assemblies in a Model of the Brain. 499-504 - Amit Daniely, Elad Granot:
On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation. 505-517 - Amit Daniely, Mariano Schain, Gilad Yehudai:
RedEx: Beyond Fixed Representation Methods via Convex Optimization. 518-543 - Pramith Devulapalli, Steve Hanneke:
The Dimension of Self-Directed Learning. 544-573 - Shaun M. Fallat, Valerii Maliuk, Seyed Ahmad Mojallal, Sandra Zilles:
Learning Hypertrees From Shortest Path Queries. 574-589 - Nave Frost, Zachary C. Lipton, Yishay Mansour, Michal Moshkovitz:
Partially Interpretable Models with Guarantees on Coverage and Accuracy. 590-613 - Germano Gabbianelli, Gergely Neu, Matteo Papini:
Importance-Weighted Offline Learning Done Right. 614-634 - Amin Karbasi, Kasper Green Larsen:
The Impossibility of Parallelizing Boosting. 635-653 - Ari Karchmer:
Agnostic Membership Query Learning with Nontrivial Savings: New Results and Techniques. 654-682 - Ian A. Kash, Lev Reyzin, Zishun Yu:
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs. 683-718 - Steve Hanneke, Aryeh Kontorovich, Guy Kornowski:
Efficient Agnostic Learning with Average Smoothness. 719-731 - Fangshuo Liao, Anastasios Kyrillidis:
Provable Accelerated Convergence of Nesterov's Momentum for Deep ReLU Neural Networks. 732-784 - Hang Liao, Deeparnab Chakrabarty:
Learning Spanning Forests Optimally in Weighted Undirected Graphs with CUT queries. 785-807 - Kabir Aladin Verchand, Mengqi Lou, Ashwin Pananjady:
Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization. 808-809 - Zhou Lu:
On the Computational Benefit of Multimodal Learning. 810-821 - Anqi Mao, Mehryar Mohri, Yutao Zhong:
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms. 822-867 - Michael Menart, Enayat Ullah, Raman Arora, Raef Bassily, Cristóbal Guzmán:
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates. 868-906 - Gergely Neu, Julia Olkhovskaya, Sattar Vakili:
Adversarial Contextual Bandits Go Kernelized. 907-929 - Chirag Pabbaraju:
Multiclass Learnability Does Not Imply Sample Compression. 930-944 - Stephen Pasteris, Fabio Vitale, Mark Herbster, Claudio Gentile, André Panisson:
Adversarial Online Collaborative Filtering. 945-971 - Binghui Peng, Christos H. Papadimitriou:
The complexity of non-stationary reinforcement learning. 972-996 - Ananth Raman, Vinod Raman, Unique Subedi, Idan Mehalel, Ambuj Tewari:
Multiclass Online Learnability under Bandit Feedback. 997-1012 - Amitis Shidani, Sattar Vakili:
Optimal Regret Bounds for Collaborative Learning in Bandits. 1013-1029 - Vikrant Singhal:
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions. 1030-1054 - Stefan Stojanovic, Konstantin Donhauser, Fanny Yang:
Tight bounds for maximum ℓ1-margin classifiers. 1055-1112 - Unique Subedi, Vinod Raman, Ambuj Tewari:
Online Infinite-Dimensional Regression: Learning Linear Operators. 1113-1133 - Puoya Tabaghi, Yusu Wang:
Universal Representation of Permutation-Invariant Functions on Vectors and Tensors. 1134-1187 - Shlomi Weitzman, Sivan Sabato:
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies. 1188-1207 - Zhiyu Zhang, Heng Yang, Ashok Cutkosky, Ioannis Ch. Paschalidis:
Improving Adaptive Online Learning Using Refined Discretization. 1208-1233 - Shiliang Zuo:
Corruption-Robust Lipschitz Contextual Search. 1234-1254
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last updated on 2024-11-30 00:03 CET by the dblp team
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