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Gen Li
Gen Li
Statistics and Data Science, Wharton School, University of Pennsylvania
Verified email at wharton.upenn.edu - Homepage
Title
Cited by
Cited by
Year
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
742020
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
542020
Nonconvex low-rank tensor completion from noisy data
C Cai, G Li, HV Poor, Y Chen
Advances in neural information processing systems 32, 2019
482019
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
462021
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
272021
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
272017
Active orthogonal matching pursuit for sparse subspace clustering
Y Chen, G Li, Y Gu
IEEE Signal Processing Letters 25 (2), 164-168, 2017
262017
Phase retrieval using iterative projections: Dynamics in the large systems limit
G Li, Y Gu, YM Lu
2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015
242015
Is Q-learning minimax optimal? a tight sample complexity analysis
G Li, C Cai, Y Chen, Y Gu, Y Wei, Y Chi
arXiv preprint arXiv:2102.06548, 2021
172021
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
14*2021
A general framework for understanding compressed subspace clustering algorithms
L Meng, G Li, J Yan, Y Gu
IEEE Journal of Selected Topics in Signal Processing 12 (6), 1504-1519, 2018
92018
Rigorous restricted isometry property of low-dimensional subspaces
G Li, Q Liu, Y Gu
Applied and Computational Harmonic Analysis 49 (2), 608-635, 2020
82020
Distance-preserving property of random projection for subspaces
G Li, Y Gu
2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017
82017
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, 2021
72021
Sample-efficient reinforcement learning is feasible for linearly realizable MDPs with limited revisiting
G Li, Y Chen, Y Chi, Y Gu, Y Wei
Advances in Neural Information Processing Systems 34, 2021
7*2021
Tightening the dependence on horizon in the sample complexity of Q-learning
G Li, C Cai, Y Chen, Y Gu, Y Wei, Y Chi
International Conference on Machine Learning, 6296-6306, 2021
62021
An RIP-based performance guarantee of covariance-assisted matching pursuit
J Wang, G Li, L Rencker, W Wang, Y Gu
IEEE Signal Processing Letters 25 (6), 828-832, 2018
62018
Subspace principal angle preserving property of gaussian random projection
Y Jiao, X Shen, G Li, Y Gu
2018 IEEE Data Science Workshop (DSW), 115-119, 2018
52018
Spectral initialization for nonconvex estimation: High-dimensional limit and phase transitions
YM Lu, G Li
2017 IEEE International Symposium on Information Theory (ISIT), 3015-3019, 2017
52017
Minimax estimation of linear functions of eigenvectors in the face of small eigen-gaps
G Li, C Cai, Y Gu, HV Poor, Y Chen
arXiv preprint arXiv:2104.03298, 2021
42021
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Articles 1–20