| 2024 | On Computationally Efficient Multi-Class Calibration. | Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum |
| 2024 | Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract. | Toms Gonzlez, Cristbal Guzmn, Courtney Paquette |
| 2024 | Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. | Noah Golowich, Ankur Moitra |
| 2024 | On Convex Optimization with Semi-Sensitive Features. | Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang |
| 2024 | (ε, u)-Adaptive Regret Minimization in Heavy-Tailed Bandits. | Gianmarco Genalti, Lupo Marsigli, Nicola Gatti, Alberto Maria Metelli |
| 2024 | Adversarial Online Learning with Temporal Feedback Graphs. | Khashayar Gatmiry, Jon Schneider |
| 2024 | Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier. | Khashayar Gatmiry, Jonathan A. Kelner, Santosh S. Vempala |
| 2024 | Safe Linear Bandits over Unknown Polytopes. | Aditya Gangrade, Tianrui Chen, Venkatesh Saligrama |
| 2024 | Agnostic Active Learning of Single Index Models with Linear Sample Complexity. | Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li |
| 2024 | Online Newton Method for Bandit Convex Optimisation Extended Abstract. | Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo |
| 2024 | Computation-information gap in high-dimensional clustering. | Bertrand Even, Christophe Giraud, Nicolas Verzelen |
| 2024 | Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo (Extended Abstract). | Amedeo Roberto Esposito, Marco Mondelli |
| 2024 | Topological Expressivity of ReLU Neural Networks. | Ekin Ergen, Moritz Grillo |
| 2024 | The Real Price of Bandit Information in Multiclass Classification. | Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran |
| 2024 | An information-theoretic lower bound in time-uniform estimation. | John C. Duchi, Saminul Haque |
| 2024 | Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models. | Maximilien Dreveton, Alperen Gzeten, Matthias Grossglauser, Patrick Thiran |
| 2024 | Physics-informed machine learning as a kernel method. | Nathan Doumche, Francis R. Bach, Grard Biau, Claire Boyer |
| 2024 | On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective. | Daniil Dmitriev, Kristf Szab, Amartya Sanyal |
| 2024 | Statistical Query Lower Bounds for Learning Truncated Gaussians. | Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis |
| 2024 | Testable Learning of General Halfspaces with Adversarial Label Noise. | Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Nikos Zarifis |
| 2024 | Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials. | Ilias Diakonikolas, Daniel M. Kane |
| 2024 | Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"? | Constantinos Daskalakis, Noah Golowich |
| 2024 | Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). | Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna |
| 2024 | Statistical curriculum learning: An elimination algorithm achieving an oracle risk. | Omer Cohen, Ron Meir, Nir Weinberger |
| 2024 | Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. | Edith Cohen, Xin Lyu, Jelani Nelson, Tams Sarls, Uri Stemmer |