I am a third-year PhD student in Computer Science at Yale University, where I am fortunate to be advised by Prof. Yang Cai. I received my bachelor’s degree in EECS at Peking University, where I was fortunate to be advised by Prof. Xiaotie Deng. During my undergraduate study, I also worked closely with Prof. Lirong Xia.

I have a broad interest in theoretical computer science, optimization, machine learning, and economics. Currently I focus on min-max optimization and online learning in games, and try to provide theoretical understanding of deep learning and reinforcement learning.

  • Algorithmic Game theory
  • Online Learning
  • Min-Max Optimization
  • BSc (Summa Cum Laude) in Computer Science, 2021

    Peking University

Working Papers

(2024). Tractable Local Equilibria in Non-Concave Games. Working Paper.

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(2023). Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games. Working Paper.

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(2022). Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion. Working Paper. NeurIPS 2022 OPT Workshop.

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(2024). Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games. The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), Oral Presentation.

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(2024). Learning Thresholds with Latent Values and Censored Feedback. The Twelfth International Conference on Learning Representations (ICLR).

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(2023). Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS).

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(2023). Doubly Optimal No-Regret Learning in Monotone Games. Proceedings of the 40th International Conference on Machine Learning (ICML).

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(2023). Accelerated Single-Call Methods for Constrained Min-Max Optimization. Proceedings of the 11th International Conference on Learning Representations (ICLR).

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(2022). Beyond the Worst Case: Semi-random Complexity Analysis of Winner Determination. The 18th Conference on Web and Internet Economics (WINE).

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(2022). Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS) Oral Presentation.

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(2022). Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions. Proceedings of the ACM Web Conference (WWW).

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(2022). Revenue and User Traffic Maximization in Mobile Short-Video Advertising. Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

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(2021). The Smoothed Complexity of Computing Kemeny and Slater Rankings. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

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Visiting Graduate Student
January 2022 – May 2022 California, U.S.
Participate the Learning and Games Program.
Research Assistant
May 2019 – September 2021 Beijing, China
Advised by Xiaotie Deng.