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
Selected recent papers
Reinforcement learning
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 (INFORMS George Nicholson award finalist, 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]
Spectral methods
Y. Yan, Y. Chen, J. Fan, “Inference for heteroskedastic PCA with missing data,” 2021. [paper]
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]
