| Breaking the sample size barrier in model-based reinforcement learning with a generative model G Li, Y Wei, Y Chi, Y Gu, Y Chen Advances in neural information processing systems 33, 12861-12872, 2020 | 169 | 2020 |
| Sample complexity of asynchronous Q-learning: Sharper analysis and variance reduction G Li, Y Wei, Y Chi, Y Gu, Y Chen IEEE Transactions on Information Theory 68 (1), 448-473, 2021 | 167 | 2021 |
| Towards non-asymptotic convergence for diffusion-based generative models G Li, Y Wei, Y Chen, Y Chi The Twelfth International Conference on Learning Representations, 2024 | 150* | 2024 |
| Settling the sample complexity of model-based offline reinforcement learning G Li, L Shi, Y Chen, Y Chi, Y Wei The Annals of Statistics 52 (1), 233-260, 2024 | 144 | 2024 |
| Nonconvex low-rank tensor completion from noisy data C Cai, G Li, HV Poor, Y Chen Advances in neural information processing systems 32, 2019 | 141 | 2019 |
| Is Q-learning minimax optimal? a tight sample complexity analysis G Li, C Cai, Y Chen, Y Wei, Y Chi Operations Research, 2023 | 140 | 2023 |
| Pessimistic q-learning for offline reinforcement learning: Towards optimal sample complexity L Shi, G Li, Y Wei, Y Chen, Y Chi International conference on machine learning, 19967-20025, 2022 | 140 | 2022 |
| Phase transitions of spectral initialization for high-dimensional non-convex estimation YM Lu, G Li Information and Inference: A Journal of the IMA 9 (3), 507-541, 2020 | 119 | 2020 |
| Subspace estimation from unbalanced and incomplete data matrices: ℓ2,∞ statistical guarantees C Cai, G Li, Y Chi, HV Poor, Y Chen The Annals of Statistics 49 (2), 944-967, 2021 | 94 | 2021 |
| Breaking the sample complexity barrier to regret-optimal model-free reinforcement learning G Li, L Shi, Y Chen, Y Gu, Y Chi Advances in Neural Information Processing Systems 34, 17762-17776, 2021 | 83 | 2021 |
| Accelerating convergence of score-based diffusion models, provably G Li, Y Huang, T Efimov, Y Wei, Y Chi, Y Chen International Conference on Machine Learning, 2024 | 82 | 2024 |
| Softmax policy gradient methods can take exponential time to converge G Li, Y Wei, Y Chi, Y Gu, Y Chen Conference on Learning Theory, 3107-3110, 2021 | 79 | 2021 |
| The curious price of distributional robustness in reinforcement learning with a generative model L Shi, G Li, Y Wei, Y Chen, M Geist, Y Chi Advances in Neural Information Processing Systems 36, 79903-79917, 2023 | 76 | 2023 |
| The efficacy of pessimism in asynchronous Q-learning Y Yan, G Li, Y Chen, J Fan IEEE Transactions on Information Theory 69 (11), 7185-7219, 2023 | 76 | 2023 |
| A sharp convergence theory for the probability flow odes of diffusion models G Li, Y Wei, Y Chi, Y Chen arXiv preprint arXiv:2408.02320, 2024 | 62 | 2024 |
| Adapting to unknown low-dimensional structures in score-based diffusion models G Li, Y Yan Advances in neural information processing systems, 2024 | 46 | 2024 |
| Minimax-optimal multi-agent RL in Markov games with a generative model G Li, Y Chi, Y Wei, Y Chen Advances in Neural Information Processing Systems 35, 15353-15367, 2022 | 46* | 2022 |
| Model-based reinforcement learning for offline zero-sum markov games Y Yan, G Li, Y Chen, J Fan Operations research 72 (6), 2430-2445, 2024 | 45* | 2024 |
| A non-asymptotic framework for approximate message passing in spiked models G Li, Y Wei arXiv preprint arXiv:2208.03313, 2022 | 45 | 2022 |
| Restricted isometry property of gaussian random projection for finite set of subspaces G Li, Y Gu IEEE Transactions on Signal Processing 66 (7), 1705-1720, 2017 | 43 | 2017 |
Yuxin ChenUniversity of PennsylvaniaVerified email at wharton.upenn.edu
