- Not All Learnable Distributions are Privately Learnable
Bun, Mark and Kamath, Gautam and Mouzakis, Anargyros-Georgios and Singhal, Vikrant
- On the Computational Benefit of Multimodal Learning
Lu, Zhou
- Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
Weitzman, Shlomi and Sabato, Sivan
- Multiclass Learnability Does Not Imply Sample Compression
Pabbaraju, Chirag
- Adversarial Online Collaborative Filtering
Pasteris, Stephen U and Gentile, Claudio and Herbster, Mark and Panisson, Andre and Vitale, Fabio
- Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
LOU, MENGQI and Chandrasekher, Kabir A and Pananjady, Ashwin
- Near-continuous time Reinforcement Learning for continuous state-action spaces
Croissant, Lorenzo and Abeille, Marc and Bouchard, Bruno
- A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
Singhal, Vikrant
- Dueling Optimization with a Monotone Adversary
Blum, Avrim and Gupta, Meghal and Li, Gene and Manoj, Naren Sarayu and Saha, Aadirupa and Yang, Yuanyuan
- Universal Representation of Permutation-Invariant Functions on Vectors and Tensors
Tabaghi, Puoya and Wang, Yusu
- Online Infinite-Dimensional Regression: Learning Linear Operators
Subedi, Unique and Raman, Vinod and Tewari, Ambuj
- Agnostic Membership Query Learning with Nontrivial Savings: New Results and Techniques
Karchmer, Ari
- Partially Interpretable Models with Guarantees on Coverage and Accuracy
Frost, Nave and Lipton, Zachary and Mansour, Yishay and Moshkovitz, Michal
- Corruption-Robust Lipschitz Contextual Search
Zuo, Shiliang
- Multiclass Online Learnability under Bandit Feedback
Raman, Vinod and Subedi, Unique and Tewari, Ambuj and Raman, Ananth S and Mehalel, Idan
- The complexity of non-stationary reinforcement learning
Peng, Binghui and Papadimitriou, Christos
- Optimal Regret Bounds for Collaborative Learning in Bandits
Shidani, Amitis and Vakili, Sattar
- Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
Afzali, Mohammad and Ashtiani, Hassan and Liaw, Christopher
- Improving Adaptive Online Learning Using Refined Discretization
Zhang, Zhiyu and Yang, Heng and Cutkosky, Ashok and Paschalidis, Ioannis C
- CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Agrawal, Shubhada and Mathieu, Timothée and Basu, Debabrota and Maillard, Odalric
- Learning Spanning Forests Optimally in Weighted Undirected Graphs with CUT queries
Liao, Hang and Chakrabarty, Deeparnab
- Learning bounded-degree polytrees with known skeleton
Choo, Davin and Yang, Qiping and Bhattacharyya, Arnab and Canonne, Clement L
- Efficient Agnostic Learning with Average Smoothness
Kornowski, Guy and Hanneke, Steve and Kontorovich, Aryeh
- The Attractor of the Replicator Dynamic in Zero-Sum Games
Biggar, Oliver and Shames, Iman
- On the Sample Complexity of Two-Layer Networks: Lipschitz Vs. Element-Wise Lipschitz Activation
Daniely, Amit and Granot, Elad
- Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
Kash, Ian and Reyzin, Lev and Yu, Zishun
- RedEx: Beyond Fixed Representation Methods via Convex Optimization
Yehudai, Gilad and Daniely, Amit and Schain, Mariano
- Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data
Awasthi, Pranjal and Kale, Satyen and Pensia, Ankit
- Online Recommendations for Agents with Discounted Adaptive Preferences
Brown, William and Agarwal, Arpit
- Learning Hypertrees From Shortest Path Queries
Fallat, Shaun M and Maliuk, valerii and Mojallal, Seyed Ahmad and Zilles, Sandra
- Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
Menart, Michael and Ullah, Enayat and Arora, Raman and Bassily, Raef and Guzman, Cristobal
- Concentration of empirical barycenters in metric spaces
Brunel, Victor-Emmanuel and Serres, Jordan
- Importance-Weighted Offline Learning Done Right
Gabbianelli, Germano and Neu, Gergely and Papini, Matteo
- Distances for Markov Chains, and Their Differentiation
Brugere, Tristan A and Wan, Zhengchao and Wang, Yusu
- Adversarial Contextual Bandits Go Kernelized
Neu, Gergely and Olkhovskaya, Julia and Vakili, Sattar
- Computation with Sequences of Assemblies in a Model of the Brain
Dabagia, Max and Papadimitriou, Christos and Vempala, Santosh
- Tight Bounds for Local Glivenko-Cantelli
Blanchard, Moise and Voráček, Václav
- A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Abernethy, Jacob and Agarwal, Alekh and Marinov, Teodor Vanislavov and Warmuth, Manfred K.
- Provable Accelerated Convergence of Nesterov’s Momentum for Deep ReLU Neural Networks
Liao, Fangshuo and Kyrillidis, Anastasios
- The Dimension of Self-Directed Learning
Devulapalli, Pramith and Hanneke, Steve
- The Impossibility of Parallelizing Boosting
Karbasi, Amin and Green Larsen, Kasper
- Tight bounds for maximum $\ell_1$-margin classifiers
Stojanovic, Stefan and Donhauser, Konstantin and Yang, Fanny
- Private PAC Learning May be Harder than Online Learning
Bun, Mark and Cohen, Aloni and Desai, Rathin
- Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms
Mao, Anqi and Mohri, Mehryar and Zhong, Yutao
Outstanding papers
- “The Attractor of the Replicator Dynamic in Zero-Sum Games” by Oliver Biggar and Iman Shames
- “Dueling Optimization with a Monotone Adversary” by Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, and Yuanyuan Yang
- “Multiclass Learnability Does Not Imply Sample Compression” by Chirag Pabbaraju
- “Private PAC Learning May be Harder than Online Learning” by Mark Bun, Aloni Cohen, and Rathin Desai