## Journal - **[Distributionally Robust Stochastic Optimal Control](https://arxiv.org/pdf/2406.05648)** Alexander Shapiro, **Yan Li** Operations Research Letters, accepted, 2024 - **[Rectangularity and Duality of Distributionally Robust Markov Decision Processes](https://arxiv.org/pdf/2308.11139.pdf)** **Yan Li**, Alexander Shapiro Mathematical Programming, under review - **[A Novel Catalyst Scheme for Stochastic Minimax Optimization](https://arxiv.org/abs/2311.02814)** Guanghui Lan, **Yan Li** Mathematical Programming, major revision - **[First-order Policy Optimization for Robust Policy Evaluation](https://arxiv.org/pdf/2307.15890.pdf)** **Yan Li**, Guanghui Lan Mathematical Programming, under review - **[First-order Policy Optimization for Robust Markov Decision Process](https://arxiv.org/pdf/2209.10579.pdf)** **Yan Li**, Guanghui Lan, Tuo Zhao Operations Research, major revision - **[Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data](https://arxiv.org/abs/2108.06808)** **Yan Li**, Caleb Ju, Ethan X. Fang, Tuo Zhao Transactions on Machine Learning Research, under review - **[Policy Mirror Descent Inherently Explores Action Space](https://arxiv.org/abs/2303.04386)** **Yan Li**, Guanghui Lan SIAM Journal on Optimization, accepted, 2024 - **[Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity](https://arxiv.org/abs/2201.09457)** **Yan Li**, Guanghui Lan, Tuo Zhao Mathematical Programming, 2023 Alice and John Jarvis Ph.D. Student Research Award - **[Block Policy Mirror Descent](https://arxiv.org/abs/2201.05756)** Guanghui Lan, **Yan Li**, Tuo Zhao SIAM Journal on Optimization, 2023 ## Conference - **[Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms](https://arxiv.org/pdf/2310.10810)** Alexander Bukharin, **Yan Li**, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao *Advances in Neural Information Processing Systems (NeurIPS), 2023* - **[Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits](https://arxiv.org/abs/2110.04844)** **Yan Li**, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan *International Conference on Learning Representations (ICLR), 2022* - **[Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably](https://arxiv.org/abs/2202.03535)** Tianyi Liu, **Yan Li**, Enlu Zhou, Tuo Zhao *International Conference on Artificial Intelligence and Statistics (AISTAT), 2022* - **[Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL](https://proceedings.neurips.cc/paper/2021/hash/9559fc73b13fa721a816958488a5b449-Abstract.html)** Minshuo Chen, **Yan Li**, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao *Advances in Neural Information Processing Systems (NeurIPS), 2021* - **[Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization](https://proceedings.mlr.press/v130/liu21e.html)** Tianyi Liu, **Yan Li**, Song Wei, Enlu Zhou, Tuo Zhao *International Conference on Artificial Intelligence and Statistics (AISTAT), 2021* - **[Deep Reinforcement Learning with Robust and Smooth Policy](http://proceedings.mlr.press/v119/shen20b.html)** **Yan Li**^*, Qianli Shen^*, Haoming Jiang, Zhaoran Wang, Tuo Zhao *International Conference on Machine Learning (ICML), 2020* - **[Implicit Bias of Gradient Descent based Adversarial Training on Separable Data](https://openreview.net/pdf?id=HkgTTh4FDH)** **Yan Li**, Huan Xu, Ethan X. Fang, Tuo Zhao *International Conference on Learning Representations (ICLR), 2020* - **[Toward Understanding the Importance of Noise in Training Neural Networks](https://proceedings.mlr.press/v97/zhou19d.html)** Mo Zhou, Tianyi Liu, **Yan Li**, Dachao Lin, Enlu Zhou, Tuo Zhao *International Conference on Machine Learning (ICML), 2019* - **[Non-convex Conditional Gradient Sliding](https://proceedings.mlr.press/v80/qu18a.html)** Chao Qu, **Yan Li**, Huan Xu *International Conference on Machine Learning (ICML), 2018* ## Preprints/Working Papers - **[Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning](https://arxiv.org/abs/2105.08268)** **Yan Li**, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha