| 2023 | ICLR | A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. | Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan |
| 2023 | ICML | Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. | Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan |
| 2023 | UAI | Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. | Chris Junchi Li, Michael I. Jordan |
| 2022 | AISTATS | On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. | Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan |
| 2022 | COLT | ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. | Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan |
| 2021 | COLT | Stochastic Approximation for Online Tensorial Independent Component Analysis. | Chris Junchi Li, Michael I. Jordan |
| 2020 | COLT | On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. | Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan |
| 2019 | ICML | Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory. | Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun |
| 2018 | AISTATS | Statistical Sparse Online Regression: A Diffusion Approximation Perspective. | Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun |
| 2017 | ICML | Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. | Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao |