Research

I have broad interests in modern optimization theory, with a particular focus on game-structure optimization. In my representative work ⭐, I have collaborated with my excellent coauthors to solve many fundamental problems in this area, such as the complexity of first-order bilevel optimization and high-order min(-max) optimization.

Research Highlights

• High-Order Min-(Max) Optimization. [Slides] [Github]
• First-Order Bilevel Optimization [Slides] [Github]

Preprints

1. Lesi Chen and Jingzhao Zhang, On the Condition Number Dependency in Bilevel Optimization, arXiv preprint. [arXiv 2025] ⭐

Featured Publications

The conference and journal publications that have overlapped are grouped into one item.

1. Lesi Chen, Chengchang Liu, Luo Luo, and Jingzhao Zhang, Faster Newton Methods for Convex and Nonconvex Optimization in Gradient Complexity , in Conference on Learning Theory. [COLT 2026] ⭐

2. Lesi Chen, Junru Li, El Mahdi Chayti and Jingzhao Zhang, Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization, in International Conference on Learning Representations. [ICLR 2026]

3. Lesi Chen, Chengchang Liu, Luo Luo, and Jingzhao Zhang, Solving Convex-Concave Problems with $\tilde{\mathcal{O}}(\epsilon^{-4/7})$ Second-Order Oracle Complexity, in Conference on Learning Theory. [COLT 2025] ⭐
   Best Student Paper Award. Long version with two additional authors [arXiv 2026].

4. Lesi Chen¹, Yaohua Ma¹, and Jingzhao Zhang, Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles , Journal of Machine Learning Research, 1-56. [JMLR 2025] ⭐
   Lesi Chen¹, Jing Xu¹, and Jingzhao Zhang, On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis, in Conference on Learning Theory. [COLT 2024]

5. Lesi Chen¹, Chengchang Liu¹, and Jingzhao Zhang, Second-Order Min-Max Optimization with Lazy Hessians, in International Conference on Learning Representations. (Oral, <2%) [ICLR 2025]

6. Lesi Chen and Luo Luo, Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization, Journal of Machine Learning Research, 1-44. [JMLR 2024]

7. Huaqing Zhang¹, Lesi Chen¹, Jing Xu, and Jingzhao Zhang, Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems, in Conference on Neural Information Processing Systems.
   [NeurIPS 2024]

8. Lesi Chen, Jing Xu, and Luo Luo, Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization, in International Conference on Machine Learning. [ICML 2023]

9. Lesi Chen, Boyuan Yao, and Luo Luo, Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Condition, in Conference on Neural Information Processing Systems. [NeurIPS 2022]

See Google Scholar for a complete list.