| Benchmarking model-based reinforcement learning T Wang, X Bao, I Clavera, J Hoang, Y Wen, E Langlois, S Zhang, G Zhang, ... arXiv preprint arXiv:1907.02057, 2019 | 431 | 2019 |
| Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev S Chewi, MA Erdogdu, MB Li, R Shen, M Zhang arXiv preprint arXiv:2112.12662, 2021 | 93 | 2021 |
| Towards a theory of non-log-concave sampling: first-order stationarity guarantees for langevin monte carlo K Balasubramanian, S Chewi, MA Erdogdu, A Salim, S Zhang Conference on Learning Theory, 2896-2923, 2022 | 59 | 2022 |
| One-shot pruning of recurrent neural networks by jacobian spectrum evaluation MS Zhang, B Stadie arXiv preprint arXiv:1912.00120, 2019 | 39 | 2019 |
| Convergence of Langevin Monte Carlo in chi-squared and Rényi divergence MA Erdogdu, R Hosseinzadeh, S Zhang International Conference on Artificial Intelligence and Statistics, 8151-8175, 2022 | 35 | 2022 |
| Improved discretization analysis for underdamped Langevin Monte Carlo S Zhang, S Chewi, M Li, K Balasubramanian, MA Erdogdu The Thirty Sixth Annual Conference on Learning Theory, 36-71, 2023 | 24 | 2023 |
| Convergence and optimality of policy gradient methods in weakly smooth settings MS Zhang, MA Erdogdu, A Garg Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 9066-9073, 2022 | 7 | 2022 |
| Sampling from the Mean-Field Stationary Distribution Y Kook, MS Zhang, S Chewi, MA Erdogdu, MB Li arXiv preprint arXiv:2402.07355, 2024 | 4 | 2024 |
| Tight regret and complexity bounds for thompson sampling via langevin monte carlo T Huix, M Zhang, A Durmus International Conference on Artificial Intelligence and Statistics, 8749-8770, 2023 | 4 | 2023 |
| In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies Y Kook, SS Vempala, MS Zhang arXiv preprint arXiv:2405.01425, 2024 | | 2024 |
