| 2025 | The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis. | Matthew Zurek, Yudong Chen |
| 2025 | Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit. | Julien Zhou, Pierre Gaillard, Thibaud Rahier, Julyan Arbel |
| 2025 | Preface. | |
| 2025 | How rotation invariant algorithms are fooled by noise on sparse targets. | Manfred K. Warmuth, Wojciech Kotlowski, Matt Jones, Ehsan Amid |
| 2025 | Noisy Computing of the Threshold Function. | Ziao Wang, Nadim Ghaddar, Banghua Zhu, Lele Wang |
| 2025 | Online Learning of Quantum States with Logarithmic Loss via VB-FTRL. | Wei-Fu Tseng, Kai-Chun Chen, Zi-Hong Xiao, Yen-Huan Li |
| 2025 | Clustering with bandit feedback: breaking down the computation/information gap. | Victor Thuot, Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen |
| 2025 | High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm. | Vishwak Srinivasan, Andre Wibisono, Ashia Wilson |
| 2025 | Self-Directed Node Classification on Graphs. | Georgy Sokolov, Maximilian Thiessen, Margarita Akhmejanova, Fabio Vitale, Francesco Orabona |
| 2025 | Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate. | Jie Shen |
| 2025 | The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization. | Matan Schliserman, Uri Sherman, Tomer Koren |
| 2025 | An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems. | Sarah Sachs, Hdi Hadiji, Tim van Erven, Mathias Staudigl |
| 2025 | Effective Littlestone dimension. | Valentino Delle Rose, Alexander Kozachinskiy, Tomasz Steifer |
| 2025 | A Unified Theory of Supervised Online Learnability. | Vinod Raman, Unique Subedi, Ambuj Tewari |
| 2025 | Data Dependent Regret Bounds for Online Portfolio Selection with Predicted Returns. | Sudeep Raja Putta, Shipra Agrawal |
| 2025 | On Generalization Bounds for Neural Networks with Low Rank Layers. | Andrea Pinto, Akshay Rangamani, Tomaso A. Poggio |
| 2025 | A Characterization of List Regression. | Chirag Pabbaraju, Sahasrajit Sarmasarkar |
| 2025 | Efficient Optimal PAC Learning. | Mikael Mller Hgsgaard |
| 2025 | Fast Convergence of Φ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler. | Siddharth Mitra, Andre Wibisono |
| 2025 | Center-Based Approximation of a Drifting Distribution. | Alessio Mazzetto, Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal |
| 2025 | Enhanced H-Consistency Bounds. | Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2025 | Computationally efficient reductions between some statistical models. | Mengqi Lou, Guy Bresler, Ashwin Pananjady |
| 2025 | Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression. | Jackie Lok, Rishi Sonthalia, Elizaveta Rebrova |
| 2025 | On the Hardness of Learning One Hidden Layer Neural Networks. | Shuchen Li, Ilias Zadik, Manolis Zampetakis |
| 2025 | Minimax-optimal and Locally-adaptive Online Nonparametric Regression. | Paul Liautaud, Pierre Gaillard, Olivier Wintenberger |