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.
• 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).
• 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.
• 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

• Overwhelmed by Too Many Choices When Shopping? Wharton Retail Expert Weighs In, Knowledge @ Wharton - 12/19/2024
• Michael Lee, Oatly North America’s EVP & Executive Creative Director, Knowledge @ Wharton - 12/19/2024
• Why Anchor Institutions Are Vital to Local Economies, Knowledge @ Wharton - 12/17/2024
• Is the American Dream of Homeownership Still Within Reach?, Knowledge @ Wharton - 12/17/2024
• Even Under Trump, AI Governance Is Here to Stay, Knowledge @ Wharton - 12/17/2024
• 2024 Ripple Rewind (Part 2), Knowledge @ Wharton - 12/17/2024
• What Are the Best Countries of 2024?, Knowledge @ Wharton - 12/16/2024
• Your Pride Could Affect Your Workplace Reputation, Knowledge @ Wharton - 12/12/2024
• Samantha Baker, VP of Commerce at theSkimm, Knowledge @ Wharton - 12/12/2024
• NFL Stats & Analysis with Ben Solak, Knowledge @ Wharton - 12/11/2024