| Noisy matrix completion: Understanding statistical guarantees for convex relaxation via nonconvex optimization Y Chen, Y Chi, J Fan, C Ma, Y Yan SIAM Journal on Optimization 30 (4), 3098-3121, 2020 | 203* | 2020 |
| Inference and uncertainty quantification for noisy matrix completion Y Chen, J Fan, C Ma, Y Yan Proceedings of the National Academy of Sciences 116 (46), 22931-22937, 2019 | 171 | 2019 |
| Bridging convex and nonconvex optimization in robust PCA: Noise, outliers and missing data Y Chen, J Fan, C Ma, Y Yan The Annals of Statistics 49 (5), 2948-2971, 2021 | 81 | 2021 |
| The efficacy of pessimism in asynchronous Q-learning Y Yan, G Li, Y Chen, J Fan IEEE Transactions on Information Theory, 2023 | 61 | 2023 |
| Inference for heteroskedastic PCA with missing data Y Yan, Y Chen, J Fan The Annals of Statistics 52 (2), 729-756, 2024 | 44 | 2024 |
| Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games Y Yan, G Li, Y Chen, J Fan Operations Research, 2024 | 34* | 2024 |
| Learning Gaussian mixtures using the Wasserstein–Fisher–Rao gradient flow Y Yan, K Wang, P Rigollet The Annals of Statistics 52 (4), 1774-1795, 2024 | 31 | 2024 |
| Convex and nonconvex optimization are both minimax-optimal for noisy blind deconvolution under random designs Y Chen, J Fan, B Wang, Y Yan Journal of the American Statistical Association 118 (542), 858-868, 2023 | 30* | 2023 |
| Sample-efficient reinforcement learning for linearly-parameterized MDPs with a generative model B Wang, Y Yan, J Fan Advances in Neural Information Processing Systems 34, 2021 | 25 | 2021 |
| Efficient clustering for stretched mixtures: Landscape and optimality K Wang, Y Yan, M Diaz Advances in Neural Information Processing Systems 33, 21309-21320, 2020 | 18 | 2020 |
| Minimax-optimal reward-agnostic exploration in reinforcement learning G Li, Y Yan, Y Chen, J Fan The Thirty Seventh Annual Conference on Learning Theory, 3431-3436, 2024 | 15 | 2024 |
| Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models G Li, Y Yan arXiv preprint arXiv:2405.14861, 2024 | 13 | 2024 |
| Entrywise Inference for Causal Panel Data: A Simple and Instance-Optimal Approach Y Yan, MJ Wainwright arXiv preprint arXiv:2401.13665, 2024 | 8 | 2024 |
| Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions G Li, Y Yan arXiv preprint arXiv:2409.18959, 2024 | 5 | 2024 |
| Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review Y Yan, WJ Su, J Fan | 2* | 2024 |
| Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? B Su, J Zhang, N Collina, Y Yan, D Li, K Cho, J Fan, A Roth, WJ Su arXiv preprint arXiv:2408.13430, 2024 | 1 | 2024 |
| When can weak latent factors be statistically inferred? J Fan, Y Yan, Y Zheng arXiv preprint arXiv:2407.03616, 2024 | 1 | 2024 |
| Asymptotic seed bias in respondent-driven sampling Y Yan, B Hanlon, S Roch, K Rohe Electronic Journal of Statistics 14 (1), 1577-1610, 2020 | 1 | 2020 |
| Adaptivity and Convergence of Probability Flow ODEs in Diffusion Generative Models J Tang, Y Yan arXiv preprint arXiv:2501.18863, 2025 | | 2025 |
| Inference under Staggered Adoption: Case Study of the Affordable Care Act E Xia, Y Yan, MJ Wainwright arXiv preprint arXiv:2412.09482, 2024 | | 2024 |
Jianqing FanProfessor of Statistics, Professor of Finance, Princeton Universityprinceton.edu의 이메일 확인됨