Yan Li

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 13

Top Topics

Sample Complexity

Reinforcement Learning

Top Venues

Publications

Yan Li, Guanghui Lan, Tuo Zhao
Homotopic policy mirror descent: policy convergence, algorithmic regularization, and improved sample complexity. Math. Program. 207 (1) (2024)
Guanghui Lan, Yan Li, Tuo Zhao
Block Policy Mirror Descent. SIAM J. Optim. 33 (3) (2023)
Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach. CoRR (2021)
Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL. NeurIPS (2021)
Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. CoRR (2021)
Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. AISTATS (2021)
David Joseph Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga S. Mannem, Tuo Zhao, Hua Wang
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers. CoRR (2020)
Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao
Deep Reinforcement Learning with Smooth Policy. CoRR (2020)
Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data. ICLR (2020)
Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao
Deep Reinforcement Learning with Robust and Smooth Policy. ICML (2020)
Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data. CoRR (2019)
Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao
Towards Understanding the Importance of Noise in Training Neural Networks. CoRR (2019)
Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao
Toward Understanding the Importance of Noise in Training Neural Networks. ICML (2019)