Publications

My full publications list is on Google Scholar.

Recent Preprints

• Lesi Chen, Chengchang Liu, Luo Luo, and Jingzhao Zhang, Computationally Faster Newton Methods by Lazy Evaluations , arXiv preprint, 2025 (extended from ICLR 2025) [paper]

Selected Publications

¹ indicates co-first-authors; ^# indicates undergraduate students primarily supervised by me.

1. Lesi Chen¹, Yaohua Ma^1#, and Jingzhao Zhang, Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles , Journal of Machine Learning Research (JMLR), 1-56, 2025. [paper] [code]

2. 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. [paper]
   🏆 Best Student Paper Award

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

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

5. Huaqing Zhang^1#, Lesi Chen¹, Jing Xu, and Jingzhao Zhang, Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems, in Conference on Neural Information Processing Systems (NeurIPS), 2024. [paper]

6. 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. [paper]

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

8. 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. [paper]