| The randomized midpoint method for log-concave sampling R Shen, YT Lee Advances in Neural Information Processing Systems 32, 2019 | 123 | 2019 |
| Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev S Chewi, MA Erdogdu, M Li, R Shen, S Zhang Conference on Learning Theory, 1-2, 2022 | 98 | 2022 |
| Structured logconcave sampling with a restricted gaussian oracle YT Lee, R Shen, K Tian Conference on Learning Theory, 2993-3050, 2021 | 58 | 2021 |
| Data augmentation as feature manipulation R Shen, S Bubeck, S Gunasekar International Conference on Machine Learning, 19773-19808, 2022 | 55 | 2022 |
| Logsmooth gradient concentration and tighter runtimes for Metropolized Hamiltonian Monte Carlo YT Lee, R Shen, K Tian Conference on learning theory, 2565-2597, 2020 | 50 | 2020 |
| Generalized leverage score sampling for neural networks JD Lee, R Shen, Z Song, M Wang Advances in Neural Information Processing Systems 33, 10775-10787, 2020 | 44 | 2020 |
| Sampling with Riemannian Hamiltonian Monte Carlo in a constrained space Y Kook, YT Lee, R Shen, S Vempala Advances in Neural Information Processing Systems 35, 31684-31696, 2022 | 31 | 2022 |
| Lower bounds on Metropolized sampling methods for well-conditioned distributions YT Lee, R Shen, K Tian Advances in Neural Information Processing Systems 34, 18812-18824, 2021 | 30 | 2021 |
| Near-optimal randomized exploration for tabular Markov decision processes Z Xiong, R Shen, Q Cui, M Fazel, SS Du Advances in Neural Information Processing Systems 35, 6358-6371, 2022 | 24* | 2022 |
| Positional description matters for transformers arithmetic R Shen, S Bubeck, R Eldan, YT Lee, Y Li, Y Zhang arXiv preprint arXiv:2311.14737, 2023 | 21 | 2023 |
| How to fine-tune vision models with sgd A Kumar, R Shen, S Bubeck, S Gunasekar arXiv preprint arXiv:2211.09359, 2022 | 18 | 2022 |
| On optimal early stopping: Over-informative versus under-informative parametrization R Shen, L Gao, YA Ma arXiv preprint arXiv:2202.09885, 2022 | 15 | 2022 |
| Condition-number-independent convergence rate of Riemannian Hamiltonian Monte Carlo with numerical integrators Y Kook, YT Lee, R Shen, S Vempala The Thirty Sixth Annual Conference on Learning Theory, 4504-4569, 2023 | 14 | 2023 |
| Private convex optimization in general norms S Gopi, YT Lee, D Liu, R Shen, K Tian Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023 | 13 | 2023 |
| Algorithmic aspects of the log-Laplace transform and a non-Euclidean proximal sampler S Gopi, YT Lee, D Liu, R Shen, K Tian The Thirty Sixth Annual Conference on Learning Theory, 2399-2439, 2023 | 12 | 2023 |
| Composite logconcave sampling with a restricted gaussian oracle R Shen, K Tian, YT Lee arXiv preprint arXiv:2006.05976, 2020 | 10 | 2020 |
| When is particle filtering efficient for planning in partially observed linear dynamical systems? SS Du, W Hu, Z Li, R Shen, Z Song, J Wu Uncertainty in artificial intelligence, 728-737, 2021 | 4* | 2021 |
| Film: Fill-in language models for any-order generation T Shen, H Peng, R Shen, Y Fu, Z Harchaoui, Y Choi arXiv preprint arXiv:2310.09930, 2023 | 2 | 2023 |
Yin Tat LeePaul G. Allen School of Computer Science & Engineering, University of WashingtonBestätigte E-Mail-Adresse bei uw.edu
