Yuting Wei

• 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). Related
  Links
• 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. Related
  Links
• Gen Li and Yuting Wei (Under Revision), A non-asymptotic framework for approximate message passing in spiked models. Related
  Links
• 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.
• All Research from Yuting Wei »

Current Courses

• STAT9700 - Mathematical Statistics

  Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.

  STAT9700001 ( Syllabus )

Past 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.

• STAT9700 - Mathematical Statistics

  Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and 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

• ASA Gottfried E. Noether Early Career Scholar Award, 2025
• Google Research Scholar Award, 2023
• NSF CAREER Award, 2022
• Stein Fellowship, Stanford University, 2018
• Erich L. Lehmann Citation, University of California, Berkeley, 2018

Knowledge @ Wharton

• The Hidden Financial Risks of the AI Boom, Knowledge @ Wharton - 09/04/2026
• When Teammates Become Competitors, Knowledge @ Wharton - 09/02/2026
• What Analytics Reveal About College Football’s Top Teams, Knowledge @ Wharton - 09/02/2026
• The Hidden Rules of Workplace Opportunity, Knowledge @ Wharton - 09/01/2026
• The Four Essential Elements of Successful Situations, Knowledge @ Wharton - 09/01/2026
• Can AI Productivity Grow Fast Enough to Justify Big Tech’s Spending?, Knowledge @ Wharton - 09/01/2026
• Cookstove Subsidies Fight Climate Change at a Lower Cost, Knowledge @ Wharton - 09/01/2026
• Why Bond Yields Are Warning Washington, Knowledge @ Wharton - 08/28/2026
• Archer Roose Is Making Canned Wine Mainstream, Knowledge @ Wharton - 08/27/2026
• The Bond Market Is Losing Confidence in the Fed, Knowledge @ Wharton - 08/26/2026