Yuxin Chen

I am an assistant professor of Electrical 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 with strong mathematical background and interest in the general areas of
highdimensional statistics, nonconvex optimization, and reinforcement learning.
Recent news
Teaching
Recent papers
Reinforcement learning
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 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,” 2020 (accepted to NeurIPS 2020). [paper][slides]
S. Cen, C. Cheng, Y. Chen, Y. Wei, Y. Chi, “Fast global convergence of natural policy gradient methods with entropy regularization,” 2020. [paper][slides]
Spectral methods
C. Cheng, Y. Wei, Y. Chen, “Tackling small eigengaps: Finegrained eigenvector estimation and inference under heteroscedastic noise,” 2020. [paper][slides]
C. Cai, G. Li, Y. Chi, H. V. Poor, Y. Chen, “Subspace estimation from unbalanced and incomplete data matrices:
statistical guarantees,” accepted to Annals of Statistics, 2020. [paper]
Y. Chen, C. Cheng, J. Fan, “Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed lowrank matrices,” accepted to Annals of Statistics, 2020. [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]
