Yuxin Chen

I am an assistant professor of Electrical and Computer Engineering, and an associated faculty member of Computer Science, Applied and Computational Mathematics, and the Center for Statistics and Machine Learning at Princeton University.
Prior to joining Princeton in Spring 2017, I was a postdoctoral scholar in the Department of Statistics at Stanford University supervised by Prof. Emmanuel Candès. I completed my Ph.D. in Electrical Engineering at Stanford University in Fall 2014, under the supervision of Prof. Andrea Goldsmith.
Research areas: mathematical data science, statistics, reinforcement learning, optimization, information theory, and their applications to medical imaging and computational biology.
Contact:
C330, Engineering Quad
Princeton University, Princeton, NJ 08544
Email: yuxin dot chen at princeton dot edu

Openings
I'm looking for highly motivated postdocs and Ph. D. students with strong mathematical background and interest in the general areas of
statistics, optimization and reinforcement learning.
Recent news
Teaching
Recent papers
Reinforcement learning
G. Li, L. Shi, Y. Chen, Y. Gu, Y. Chi, “Breaking the sample complexity barrier to regretoptimal modelfree reinforcement learning,” NeurIPS 2021.
G. Li, Y. Chen, Y. Chi, Y. Gu, Y. Wei, “Sampleefficient reinforcement learning is feasible for linearly realizable MDPs with limited revisiting,” NeurIPS 2021.
W. Zhan*, S. Cen*, B. Huang, Y. Chen, J. D. Lee, Y. Chi, “Policy mirror descent for regularized reinforcement learning: A generalized framework with linear convergence,” 2021. (*=equal contributions)
G. Li, Y. Wei, Y. Chi, Y. Gu, Y. Chen, “Softmax policy gradient methods can take exponential time to converge,” 2021 (accepted in part to COLT 2021). [slides]
G. Li, C. Cai, Y. Chen, Y. Gu, Y. Wei, Y. Chi, “Is QLearning Minimax Optimal? A Tight Sample Complexity Analysis,” 2021 (accepted in part to ICML 2021).
S. Cen, C. Cheng, Y. Chen, Y. Wei, Y. Chi, “Fast global convergence of natural policy gradient methods with entropy regularization,” accepted to Operations Research, 2021. [paper][slides]
G. Li, Y. Wei, Y. Chi, Y. Gu, Y. Chen, “Breaking the sample size barrier in modelbased reinforcement learning with a generative model,” 2020 (accepted in part to NeurIPS 2020). [paper][slides]
G. Li, Y. Wei, Y. Chi, Y. Gu, Y. Chen, “Sample complexity of asynchronous Qlearning: Sharper analysis and variance reduction,” accepted to IEEE Transactions on Information Theory, 2021 (appeared in part to NeurIPS 2020). [paper][slides]
Spectral methods
G. Li, C. Cai, Y. Gu, H. V. Poor, Y. Chen, “Minimax estimation of linear functions of eigenvectors in the face of small eigengaps,” 2021. [paper]
C. Cheng, Y. Wei, Y. Chen, “Tackling small eigengaps: Finegrained eigenvector estimation and inference under heteroscedastic noise,” accepted to IEEE Transactions on Information Theory, 2020. [paper][slides]
C. Cai, G. Li, Y. Chi, H. V. Poor, Y. Chen, “Subspace estimation from unbalanced and incomplete data matrices, $\ell_{2,\infty}$ statistical guarantees ” Annals of Statistics, vol. 49, no. 2, pp. 944967, 2021. [paper]
Y. Chen, C. Cheng, J. Fan, “Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed lowrank matrices,” Annals of Statistics, vol. 49, no. 1, pp. 435458, 2021. [paper][slides]
Y. Chen, J. Fan, C. Ma, K. Wang, “Spectral method and regularized MLE are both optimal for topK ranking,” Annals of Statistics, vol. 47, no. 4, pp. 22042235, August 2019. [Arxiv][slides]
