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.
Interests
- Algorithmic Game theory
- Online Learning
- Min-Max Optimization
Education
BSc (Summa Cum Laude) in Computer Science, 2021
Peking University
Working Papers
(2024).
COMAL: A Convergent Meta Algorithm for Aligning LLMs with General Preferences.
NeurIPS workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability (FITML). Selected for Oral Presentation.
(2023).
Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games.
Working Paper.
Publications
(2024).
On Tractable $\Phi$-Equilibria in Non-Concave Games.
The 38th Annual Conference on Neural Information Processing Systems (NeurIPS).
(2024).
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms.
The 38th Annual Conference on Neural Information Processing Systems (NeurIPS).
(2024).
Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion.
The Forty-first International Conference on Machine Learning (ICML).
(2024).
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games.
The 27th International Conference on Artificial Intelligence and Statistics (AISTATS). Selected for Oral Presentation.
(2024).
Learning Thresholds with Latent Values and Censored Feedback.
The Twelfth International Conference on Learning Representations (ICLR).
(2023).
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback.
The 37th Annual Conference on Neural Information Processing Systems (NeurIPS).
(2023).
Doubly Optimal No-Regret Learning in Monotone Games.
Proceedings of the 40th International Conference on Machine Learning (ICML).
(2023).
Accelerated Single-Call Methods for Constrained Min-Max Optimization.
Proceedings of the 11th International Conference on Learning Representations (ICLR).
(2022).
Beyond the Worst Case: Semi-random Complexity Analysis of Winner Determination.
The 18th Conference on Web and Internet Economics (WINE).
(2022).
Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games.
The 36th Annual Conference on Neural Information Processing Systems (NeurIPS). Selected for Oral Presentation.
(2022).
Tight Last-Iterate Convergence of the Extragradient and the Optimistic Gradient Descent-Ascent Algorithm for Constrained Monotone Variational Inequalities.
Preliminary Version of the NeurIPS 2022 Paper Above.
(2022).
Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions.
Proceedings of the ACM Web Conference (WWW).
(2022).
Revenue and User Traffic Maximization in Mobile Short-Video Advertising.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
(2021).
The Smoothed Complexity of Computing Kemeny and Slater Rankings.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
Experience
Visiting Graduate Student
Participate the Learning and Games Program.
Research Assistant
Advised by Xiaotie Deng.
Contact
- weiqiang.zheng@yale.edu
- 17 Hillhouse Avenue, Room 333, New Haven, CT 06511