| 2023 | Generalization Guarantees via Algorithm-dependent Rademacher Complexity. | Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli |
| 2023 | Find a witness or shatter: the landscape of computable PAC learning. | Valentino Delle Rose, Alexander Kozachinskiy, Cristbal Rojas, Tomasz Steifer |
| 2023 | The k-Cap Process on Geometric Random Graphs. | Mirabel E. Reid, Santosh S. Vempala |
| 2023 | Exploring Local Norms in Exp-concave Statistical Learning. | Nikita Puchkin, Nikita Zhivotovskiy |
| 2023 | Near-optimal fitting of ellipsoids to random points. | Aaron Potechin, Paxton M. Turner, Prayaag Venkat, Alexander S. Wein |
| 2023 | Kernelized Diffusion Maps. | Loucas Pillaud-Vivien, Francis R. Bach |
| 2023 | Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. | Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh |
| 2023 | InfoNCE Loss Provably Learns Cluster-Preserving Representations. | Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai |
| 2023 | Sparse PCA Beyond Covariance Thresholding. | Gleb Novikov |
| 2023 | PAC Verification of Statistical Algorithms. | Saachi Mutreja, Jonathan Shafer |
| 2023 | Sparsity-aware generalization theory for deep neural networks. | Ramchandran Muthukumar, Jeremias Sulam |
| 2023 | Local Risk Bounds for Statistical Aggregation. | Jaouad Mourtada, Tomas Vaskevicius, Nikita Zhivotovskiy |
| 2023 | Sharp thresholds in inference of planted subgraphs. | Elchanan Mossel, Jonathan Niles-Weed, Youngtak Sohn, Nike Sun, Ilias Zadik |
| 2023 | List Online Classification. | Shay Moran, Ohad Sharon, Iska Tsubari, Sivan Yosebashvili |
| 2023 | Efficient median of means estimator. | Stanislav Minsker |
| 2023 | Quasi-Newton Steps for Efficient Online Exp-Concave Optimization. | Zakaria Mhammedi, Khashayar Gatmiry |
| 2023 | Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. | David Martnez-Rubio, Elias Samuel Wirth, Sebastian Pokutta |
| 2023 | Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. | David Martnez-Rubio, Sebastian Pokutta |
| 2023 | Active Coverage for PAC Reinforcement Learning. | Aymen Al Marjani, Andrea Tirinzoni, Emilie Kaufmann |
| 2023 | Detection-Recovery Gap for Planted Dense Cycles. | Cheng Mao, Alexander S. Wein, Shenduo Zhang |
| 2023 | Shortest Program Interpolation Learning. | Naren Sarayu Manoj, Nathan Srebro |
| 2023 | Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations. | Oren Mangoubi, Nisheeth K. Vishnoi |
| 2023 | Learning Hidden Markov Models Using Conditional Samples. | Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang |
| 2023 | Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise. | Zijian Liu, Jiawei Zhang, Zhengyuan Zhou |
| 2023 | Exponential Hardness of Reinforcement Learning with Linear Function Approximation. | Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellrt Weisz, Csaba Szepesvri |