Yuting Wei

Associate Professor of Statistics and Data Science

Contact Information

Primary Email:
ytwei@wharton.upenn.edu

office Address:
307 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104

Research Interests: high-dimensional and nonparametric statistics, statistical inference, reinforcement learning, statistical genetics

Links: Personal Website

Overview

Education

Ph.D. in Statistics, University of California at Berkeley, 2018
Advisors: Martin Wainwright, Aditya Guntuboyina

B.S. in Statistics, Peking University, 2013

B.A. in Economics, Peking University, 2013

Academic Positions Held

Assistant Professor, Department of Statistics and Data Science,
the Wharton School, University of Pennsylvania, 2021-present

Assistant Professor, Department of Statistics and Data Science,
Carnegie Mellon University, 2019-2021

Stein’s Fellow / Lecturer, Statistics Department,
Stanford University, 2018-2019

Research

Gen Li, Weichen Wu, Yuejie Chi, Cong Ma, Alessandro Rinaldo, Yuting Wei (2024), High-Probability Sample Complexities for Policy Evaluation With Linear Function Approximation, IEEE Transactions on Information Theory , 70 (8), pp. 5969-5999.
Gen Li, Laixi Shi, Yuxin Chen, Yuejie Chen, Yuting Wei (2024), Settling the sample complexity of model-based offline reinforcement learning, The Annals of Statistics, 52 (1), pp. 233-260.
Shicong Cen, Yuting Wei, Yuejie Chi (2024), Fast policy extragradient methods for competitive games with entropy regularization, Journal of Machine Learning Research , 25 (), pp. 1-48.
Michael Celentano, Andrea Montanari, Yuting Wei (2023), The Lasso with general Gaussian designs with applications to hypothesis testing, Annals of Statistics , 51 (5), pp. 2194-2220.
Gen Li, Wei Fan, Yuting Wei (2023), Approximate message passing from random initialization with applications to Z2 synchronization, Proceedings of the National Academy of Science , 120 (31).
Gen Li, Changxiao Cai, Yuxin Chen, Yuantao Gu, Yuting Wei, Yuejie Chi (2023), Is Q-Learning minimax optimal? A tight sample complexity analysis, Operations Research , 72 (1), pp. 222-236.
Pratik Patil, Arun Kumar Kuchibhotla, Yuting Wei, Alessandro Rinaldo (Under Revision), Mitigating multiple descents: A model-agnostic framework for risk monotonization.
Gen Li and Yuting Wei (Under Revision), A non-asymptotic framework for approximate message passing in spiked models.
Gen Li, Yuejie Chi, Yuting Wei, Yuxin Chen, Minimax-Optimal Multi-Agent RL in Zero-Sum Markov Games With a Generative Model.
Shicong Cen, Chen Cheng, Yuxin Chen, Yuting Wei, Yuejie Chi (2022), Fast global convergence of natural policy gradient methods with entropy regularization, Operations Research, 7 (4), pp. 2563-2578.

Teaching

Current Courses (Fall 2025)

STAT4300 - Probability
Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.
STAT4300003 ( Syllabus )
STAT4300004 ( Syllabus )

All Courses

MATH4990 - Supervised Study
Study under the direction of a faculty member. Intended for a limited number ofmathematics majors.
STAT4300 - Probability
Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.
STAT9910 - Sem in Adv Appl of Stat
This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.
STAT9950 - Dissertation
Dissertation

Activity

Latest Research

Gen Li, Weichen Wu, Yuejie Chi, Cong Ma, Alessandro Rinaldo, Yuting Wei (2024), High-Probability Sample Complexities for Policy Evaluation With Linear Function Approximation, IEEE Transactions on Information Theory , 70 (8), pp. 5969-5999.

All Research