Chung-Wei Lee

PhD candidate in Computer Science at USC
[Google Scholar]
[CV]
[email protected]

I am a fifth-year PhD student in Computer Science at University of Southern California. I am very fortunate to be advised by Prof. Haipeng Luo. Currently, I am a research intern at DeepMind in London, hosted by Yasin Abbasi-Yadkori. I received my B.S. from National Taiwan University, double majoring in Electrical Engineering and Mathematics. My research interests lie in theoretical machine learning and algorithmic game theory.

I am expected to graduate in May 2023 and am looking for a full-time position starting mid-2023.


Work Experience


09/2022 - present

Research Intern at DeepMind in London

05/2022 - 08/2022

Research Intern at Google Research in Mountain View

01/2022 - 05/2022

Research Intern at Meta AI in Menlo Park

05/2021 - 08/2021

Research Intern at ByteDance in Mountain View


Publications

Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games.
Gabriele Farina, Chung-Wei Lee, Haipeng Luo, Christian Kroer
International Conference on Machine Learning (ICML) 2022.
[arxiv]

Last-iterate Convergence in Extensive-form Games
Chung-Wei Lee, Christian Kroer, Haipeng Luo
Conference on Neural Information Processing Systems (NeurIPS) 2021.
[arxiv]

Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses
Haipeng Luo, Chen-Yu Wei, Chung-Wei Lee
Conference on Neural Information Processing Systems (NeurIPS) 2021.
[arxiv]

Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang, Xiaojin Zhang
International Conference on Machine Learning (ICML) 2021.
[arxiv]

Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo
Annual Conference on Learning Theory (COLT) 2021.
[arxiv]

Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo
Conference on Learning Representations (ICLR) 2021.
[arxiv]

Bias No More: High-probability Data-dependent Regret Bounds for Adversarial Bandits and MDPs
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang
Conference on Neural Information Processing Systems (NeurIPS) 2020. (Oral)
[arxiv]


A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
Chung-Wei Lee, Haipeng Luo, Mengxiao Zhang
Annual Conference on Learning Theory (COLT) 2020.
[arxiv]


A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
Yifang Chen, Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei
Annual Conference on Learning Theory (COLT) 2019.
[arxiv]

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
[arxiv]