Yuting Wei

• Pratik Patil, Arun Kumar Kuchibhotla, Yuting Wei, Alessandro Rinaldo (Under Revision), Mitigating multiple descents: A model-agnostic framework for risk monotonization. Related
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• Gen Li, Wei Fan, Yuting Wei (Under Revision), Approximate message passing from random initialization with applications to Z2 synchronization. Related
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• Gen Li and Yuting Wei (Under Revision), A non-asymptotic framework for approximate message passing in spiked models. Related
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• Gen Li, Yuejie Chi, Yuting Wei, Yuxin Chen, Minimax-Optimal Multi-Agent RL in Zero-Sum Markov Games With a Generative Model.
• Gen Li, Laixi Shi, Yuxin Chen, Yuejie Chi, Yuting Wei (Under Review), Settling the Sample Complexity of Model-Based Offline Reinforcement Learning.
• 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.
• Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi (2022), Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity, International Conference on Machine Learning (ICML).
• Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen (2022), Sample complexity of asynchronous Q-learning: sharper analysis and variance reduction, IEEE Transactions on Information Theory, 68 (1), pp. 448-473. Abstract

  Gen

• Yue Li and Yuting Wei (Under Review), Minimum L1 interpolators: Precise asymptotics and multiple descent.
• Gen Li, Yuxin Chen, Yuejie Chi, Yuantao Gu, Yuting Wei (2021), Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting, Neural Information Processing Systems (NeurIPS).
• All Research from Yuting Wei »

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.

• STAT9910 - Sem in Adv Appl of Stat

  This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

• STAT9950 - Dissertation

  Dissertation

• 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

• NBA Playoffs With Seth Partnow, Knowledge @ Wharton - 05/07/2025
• The Trade War Playbook: Tariffs, Uncertainty, and U.S. Economic Policy, Knowledge @ Wharton - 05/07/2025
• How AI Can Unlock Hybrid Creativity in the Workplace, Knowledge @ Wharton - 05/06/2025
• Are DoorDash and Other Delivery Apps Hurting Restaurants?, Knowledge @ Wharton - 05/06/2025
• Workplace Boundaries and Social Media | Nancy Rothbard, Knowledge @ Wharton - 05/06/2025
• Five Ways Gen AI Is Changing Workplace Identity, Knowledge @ Wharton - 05/05/2025
• How Consumers Perceive Layoffs: Offshoring vs. Automation, Knowledge @ Wharton - 05/02/2025
• Understanding Trump’s Tariff Tactics Through the Lens of Negotiation, Knowledge @ Wharton - 04/30/2025
• NFL Draft Strategy With Kevin Cole, Knowledge @ Wharton - 04/30/2025
• Why Fed Independence Is Critical, Knowledge @ Wharton - 04/29/2025