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ORCIDGen Li 0005
Person information
- affiliation: Chinese University of Hong Kong, Department of Statistics, Hong Kong
- affiliation (former): University of Pennsylvania, Wharton School, Department of Statistics and Data Science, Philadelphia, PA, USA
- affiliation (former): Tsinghua University, Department of Electronic Engineering / TNList, Beijing, China
Other persons with the same name
- Gen Li — disambiguation page
- Gen Li 0001 — Beijing University of Posts and Telecommunications
- Gen Li 0002 — National University of Defense Technology, Changsha, China
- Gen Li 0003 — Tianjin University, China
- Gen Li 0004
![[0000-0002-7298-2141]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-7298-2141")
— University of Michigan, Ann Arbor, MI, USA (and 2 more) - Gen Li 0006 — Cardiff University, School of Engineering, UK
- Gen Li 0007 — Genetalks Biotech Inc., Beijing, China
- Gen Li 0008 — University of Edinburgh, UK
- Gen Li 0009 — Chinese Academy of Sciences, Guangzhou Institute of Advanced Technology, China (and 1 more)
- Gen Li 0010 — ETH Zurich, Zurich, Switzerland
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2020 – today
- 2024
[j16]Gen Li, Yuting Wei, Yuejie Chi, Yuxin Chen:
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. Oper. Res. 72(1): 203-221 (2024)- [j15]Gen Li, Changxiao Cai, Yuxin Chen, Yuting Wei, Yuejie Chi:
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis. Oper. Res. 72(1): 222-236 (2024) - [j14]Gen Li, Weichen Wu, Yuejie Chi, Cong Ma, Alessandro Rinaldo, Yuting Wei:
High-Probability Sample Complexities for Policy Evaluation With Linear Function Approximation. IEEE Trans. Inf. Theory 70(8): 5969-5999 (2024) - [c24]Gen Li, Yuling Yan, Yuxin Chen, Jianqing Fan:
Minimax-optimal reward-agnostic exploration in reinforcement learning. COLT 2024: 3431-3436 - [c23]Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi:
Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models. ICLR 2024 - [c22]Gen Li, Yu Huang, Timofey Efimov, Yuting Wei, Yuejie Chi, Yuxin Chen:
Accelerating Convergence of Score-Based Diffusion Models, Provably. ICML 2024 - [i40]Gen Li, Yuting Wei:
A non-asymptotic distributional theory of approximate message passing for sparse and robust regression. CoRR abs/2401.03923 (2024) - [i39]Gen Li, Zhihan Huang, Yuting Wei:
Towards a mathematical theory for consistency training in diffusion models. CoRR abs/2402.07802 (2024) - [i38]Gen Li, Yu Huang, Timofey Efimov, Yuting Wei, Yuejie Chi, Yuxin Chen:
Accelerating Convergence of Score-Based Diffusion Models, Provably. CoRR abs/2403.03852 (2024) - [i37]Gen Li, Yuting Wei, Yuejie Chi, Yuxin Chen:
A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models. CoRR abs/2408.02320 (2024) - 2023
- [j13]Gen Li, Yuting Wei, Yuejie Chi, Yuxin Chen:
Softmax policy gradient methods can take exponential time to converge. Math. Program. 201(1): 707-802 (2023) - [j12]Gen Li, Ganghua Wang, Jie Ding:
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. IEEE Trans. Inf. Theory 69(9): 5921-5935 (2023) - [j11]Yuling Yan, Gen Li, Yuxin Chen, Jianqing Fan:
The Efficacy of Pessimism in Asynchronous Q-Learning. IEEE Trans. Inf. Theory 69(11): 7185-7219 (2023) - [j10]Gen Li, Jie Ding:
Towards Understanding Variation-Constrained Deep Neural Networks. IEEE Trans. Signal Process. 71: 631-640 (2023) - [c21]Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen:
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. NeurIPS 2023 - [c20]Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Matthieu Geist, Yuejie Chi:
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model. NeurIPS 2023 - [i36]Gen Li, Yanxi Chen, Yuejie Chi, H. Vincent Poor, Yuxin Chen:
Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods. CoRR abs/2301.13006 (2023) - [i35]Gen Li, Wei Fan, Yuting Wei:
Approximate message passing from random initialization with applications to ℤ2 synchronization. CoRR abs/2302.03682 (2023) - [i34]Gen Li, Yuling Yan, Yuxin Chen, Jianqing Fan:
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning. CoRR abs/2304.07278 (2023) - [i33]Gen Li, Ganghua Wang, Jie Ding:
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization. CoRR abs/2305.04267 (2023) - [i32]Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen:
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. CoRR abs/2305.10282 (2023) - [i31]Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Matthieu Geist, Yuejie Chi:
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model. CoRR abs/2305.16589 (2023) - [i30]Gen Li, Weichen Wu, Yuejie Chi, Cong Ma, Alessandro Rinaldo, Yuting Wei:
Sharp high-probability sample complexities for policy evaluation with linear function approximation. CoRR abs/2305.19001 (2023) - [i29]Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi:
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models. CoRR abs/2306.09251 (2023) - 2022
- [j9]Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen:
Nonconvex Low-Rank Tensor Completion from Noisy Data. Oper. Res. 70(2): 1219-1237 (2022) - [j8]Gen Li, Yuantao Gu, Jie Ding:
$\ell _1$ Regularization in Two-Layer Neural Networks. IEEE Signal Process. Lett. 29: 135-139 (2022) - [j7]Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction. IEEE Trans. Inf. Theory 68(1): 448-473 (2022) - [c19]Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi:
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity. ICML 2022: 19967-20025 - [c18]Gen Li, Yuejie Chi, Yuting Wei, Yuxin Chen:
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model. NeurIPS 2022 - [i28]Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi:
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity. CoRR abs/2202.13890 (2022) - [i27]Yuling Yan, Gen Li, Yuxin Chen, Jianqing Fan:
The Efficacy of Pessimism in Asynchronous Q-Learning. CoRR abs/2203.07368 (2022) - [i26]Gen Li, Laixi Shi, Yuxin Chen, Yuejie Chi, Yuting Wei:
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning. CoRR abs/2204.05275 (2022) - [i25]Yuling Yan, Gen Li, Yuxin Chen, Jianqing Fan:
Model-Based Reinforcement Learning Is Minimax-Optimal for Offline Zero-Sum Markov Games. CoRR abs/2206.04044 (2022) - [i24]Gen Li, Yuting Wei:
A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models. CoRR abs/2208.03313 (2022) - [i23]Gen Li, Yuejie Chi, Yuting Wei, Yuxin Chen:
Minimax-Optimal Multi-Agent RL in Zero-Sum Markov Games With a Generative Model. CoRR abs/2208.10458 (2022) - [i22]Yuyang Zhang, Runyu Zhang, Gen Li, Yuantao Gu, Na Li:
Multi-Agent Reinforcement Learning with Reward Delays. CoRR abs/2212.01441 (2022) - 2021
- [c17]Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Softmax Policy Gradient Methods Can Take Exponential Time to Converge. COLT 2021: 3107-3110 - [c16]Gen Li, Changxiao Cai, Yuxin Chen, Yuantao Gu, Yuting Wei, Yuejie Chi:
Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning. ICML 2021: 6296-6306 - [c15]Gen Li, Yuantao Gu:
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph. ICML 2021: 6337-6345 - [c14]Gen Li, Yuxin Chen, Yuejie Chi, Yuantao Gu, Yuting Wei:
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting. NeurIPS 2021: 16671-16685 - [c13]Gen Li, Laixi Shi, Yuxin Chen, Yuantao Gu, Yuejie Chi:
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning. NeurIPS 2021: 17762-17776 - [i21]Gen Li, Changxiao Cai, Yuxin Chen, Yuantao Gu, Yuting Wei, Yuejie Chi:
Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning. CoRR abs/2102.06548 (2021) - [i20]Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Softmax Policy Gradient Methods Can Take Exponential Time to Converge. CoRR abs/2102.11270 (2021) - [i19]Gen Li, Changxiao Cai, Yuantao Gu, H. Vincent Poor, Yuxin Chen:
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps. CoRR abs/2104.03298 (2021) - [i18]Gen Li, Yuxin Chen, Yuejie Chi, Yuantao Gu, Yuting Wei:
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting. CoRR abs/2105.08024 (2021) - [i17]Gen Li, Yuantao Gu, Jie Ding:
The Rate of Convergence of Variation-Constrained Deep Neural Networks. CoRR abs/2106.12068 (2021) - [i16]Gen Li, Laixi Shi, Yuxin Chen, Yuantao Gu, Yuejie Chi:
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning. CoRR abs/2110.04645 (2021) - 2020
- [j6]Xingyu Xu, Gen Li, Yuantao Gu:
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces. IEEE Trans. Signal Process. 68: 3117-3131 (2020) - [j5]Gen Li, Xingyu Xu, Yuantao Gu:
Lower Bound for RIP Constants and Concentration of Sum of Top Order Statistics. IEEE Trans. Signal Process. 68: 3169-3178 (2020) - [c12]Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction. NeurIPS 2020 - [c11]Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. NeurIPS 2020 - [i15]Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. CoRR abs/2005.12900 (2020) - [i14]Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction. CoRR abs/2006.03041 (2020) - [i13]Gen Li, Yuantao Gu, Jie Ding:
The Efficacy of L1s Regularization in Two-Layer Neural Networks. CoRR abs/2010.01048 (2020)
2010 – 2019
- 2019
- [c10]Gen Li, Jingkai Yan, Yuantao Gu:
Information Theoretic Lower Bound of Restricted Isometry Property Constant. ICASSP 2019: 5297-5301 - [c9]Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen:
Nonconvex Low-Rank Tensor Completion from Noisy Data. NeurIPS 2019: 1861-1872 - [i12]Xingyu Xu, Gen Li, Yuantao Gu:
Johnson-Lindenstrauss Property Implies Subspace Restricted Isometry Property. CoRR abs/1905.09608 (2019) - [i11]Gen Li, Xingyu Xu, Yuantao Gu:
Lower Bound for RIP Constants and Concentration of Sum of Top Order Statistics. CoRR abs/1907.06054 (2019) - [i10]Yuchen Jiao, Gen Li, Yuantao Gu:
Compressed Subspace Learning Based on Canonical Angle Preserving Property. CoRR abs/1907.06166 (2019) - [i9]Gen Li, Yuantao Gu:
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph. CoRR abs/1907.10906 (2019) - [i8]Changxiao Cai, Gen Li, Yuejie Chi, H. Vincent Poor, Yuxin Chen:
Subspace Estimation from Unbalanced and Incomplete Data Matrices: 𝓁2, ∞ Statistical Guarantees. CoRR abs/1910.04267 (2019) - [i7]Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen:
Nonconvex Low-Rank Symmetric Tensor Completion from Noisy Data. CoRR abs/1911.04436 (2019) - 2018
- [j4]Linghang Meng, Gen Li, Jingkai Yan, Yuantao Gu:
A General Framework for Understanding Compressed Subspace Clustering Algorithms. IEEE J. Sel. Top. Signal Process. 12(6): 1504-1519 (2018) - [j3]Yanxi Chen, Gen Li, Yuantao Gu:
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering. IEEE Signal Process. Lett. 25(2): 164-168 (2018) - [j2]Jiayang Wang, Gen Li, Lucas Rencker, Wenwu Wang, Yuantao Gu:
An RIP-Based Performance Guarantee of Covariance-Assisted Matching Pursuit. IEEE Signal Process. Lett. 25(6): 828-832 (2018) - [j1]Gen Li, Yuantao Gu:
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces. IEEE Trans. Signal Process. 66(7): 1705-1720 (2018) - [c8]Gen Li, Qinghua Liu, Yuantao Gu:
Restricted Isometry Property for Low-Dimensional Subspaces and its Application in Compressed Subspace Clustering. DSW 2018: 86-90 - [c7]Yuchen Jiao, Xinyue Shen, Gen Li, Yuantao Gu:
Subspace Principal Angle Preserving Property of Gaussian Random Projection. DSW 2018: 115-119 - [c6]Gen Li, Yuchen Jiao, Yuantao Gu:
Convergence Analysis on a Fast Iterative Phase Retrieval Algorithm Without Independence Assumption. ICASSP 2018: 4624-4628 - [c5]Gen Li, Jingkai Yan, Yuantao Gu:
Outage Probability Conjecture Does Not Hold for Two-Input-Multiple-Output (TIM 0) System. ISIT 2018: 1345-1349 - [i6]Gen Li, Qinghua Liu, Yuantao Gu:
Rigorous Restricted Isometry Property of Low-Dimensional Subspaces. CoRR abs/1801.10058 (2018) - 2017
- [c4]Yuchen Jiao, Gen Li, Yuantao Gu:
Principal angles preserving property of Gaussian random projection for subspaces. GlobalSIP 2017: 318-322 - [c3]Gen Li, Yuantao Gu:
Distance-preserving property of random projection for subspaces. ICASSP 2017: 3959-3963 - [c2]Yue M. Lu, Gen Li:
Spectral initialization for nonconvex estimation: High-dimensional limit and phase transitions. ISIT 2017: 3015-3019 - [i5]Yue M. Lu, Gen Li:
Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation. CoRR abs/1702.06435 (2017) - [i4]Gen Li, Yuantao Gu:
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces. CoRR abs/1704.02109 (2017) - [i3]Yanxi Chen, Gen Li, Yuantao Gu:
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering. CoRR abs/1708.04764 (2017) - [i2]Gen Li, Jingkai Yan, Yuantao Gu:
On the Outage Probability Conjecture for MIMO Channels. CoRR abs/1711.01782 (2017) - [i1]Gen Li, Yuchen Jiao, Yuantao Gu:
Linear Convergence of An Iterative Phase Retrieval Algorithm with Data Reuse. CoRR abs/1712.01712 (2017) - 2015
- [c1]Gen Li, Yuantao Gu, Yue M. Lu:
Phase retrieval using iterative projections: Dynamics in the large systems limit. Allerton 2015: 1114-1118
Coauthor Index
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