Yuxin Chen

I have joined the Department of Statistics and Data Science at the Wharton School at University of Pennsylvania since Jan. 2022, with a secondary appointment in Electrical and Systems Engineering.
I was an assistant professor of Electrical and Computer Engineering, and an associated faculty member of Computer Science and Applied and Computational Mathematics at Princeton University from 2017 to 2021.
Prior to this, I was a postdoc in Statistics at Stanford University from 2015 to 2017, and obtained my Ph.D. in Electrical Engineering at Stanford University in Jan 2015.
Research areas: mathematical data science, statistics, reinforcement learning, optimization, information theory, and their applications to medical imaging, power electronics, and computational biology.
Contact:
313 Academic Research Building
265 South 37th Street, Philadelphia, PA 19104
Email: yuxinc at wharton dot upenn dot edu

Openings
I'm looking for highly motivated postdocs and Ph. D. students with strong mathematical background and interest in machine learning theory (particularly reinforcement learning), statistics, and optimization.
Recent news
Topic courses I have developed
Selected recent papers
Reinforcement learning
G. Li, Y. Chi, Y. Wei, Y. Chen, “Minimaxoptimal multiagent RL in Markov games with a generative model,” NeurIPS 2022 (selected as oral).
G. Li, L. Shi, Y. Chen, Y. Chi, Y. Wei, “Settling the sample complexity of modelbased offline reinforcement learning,” 2022.
G. Li, Y. Wei, Y. Chi, 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. 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,” Operations Research, vol. 70, no. 4, pp. 2563–2578, 2022 (INFORMS George Nicholson award finalist, 2021). [ArXiv][OR version][slides]
G. Li, Y. Wei, Y. Chi, 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. [ArXiv]
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. [ArXiv][AoS version]
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. [ArXiv][AoS version][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][AoS version][slides]
