Li, Chris Junchi

20 publications

NeurIPSW 2024 Fast Decentralized Gradient Tracking for Federated Learning with Local Updates: From Mini to Minimax Optimization Chris Junchi Li

ICLR 2023 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael Jordan

NeurIPSW 2023 Accelerating Inexact HyperGradient Descent for Bilevel Optimization Haikuo Yang, Luo Luo, Chris Junchi Li, Michael Jordan, Maryam Fazel

ICML 2023 Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael Jordan

UAI 2023 Nonconvex Stochastic Scaled Gradient Descent and Generalized Eigenvector Problems Chris Junchi Li, Michael I Jordan

NeurIPS 2023 Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S Du

NeurIPSW 2022 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael Jordan

NeurIPS 2022 Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan

NeurIPSW 2022 Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization Chris Junchi Li, Angela Yuan, Gauthier Gidel, Michael Jordan

COLT 2022 ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm Chris Junchi Li, Wenlong Mou, Martin Wainwright, Michael Jordan

NeurIPSW 2021 Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael Jordan

COLT 2021 Stochastic Approximation for Online Tensorial Independent Component Analysis Chris Junchi Li, Michael Jordan

COLT 2020 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

ICML 2019 Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun

NeurIPS 2019 Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent Wenqing Hu, Chris Junchi Li, Xiangru Lian, Ji Liu, Huizhuo Yuan

NeurIPS 2018 SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang

AISTATS 2018 Statistical Sparse Online Regression: A Diffusion Approximation Perspective Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun

NeurIPS 2017 Diffusion Approximations for Online Principal Component Estimation and Global Convergence Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang

ICML 2017 Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao

NeurIPS 2016 Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes Chris Junchi Li, Zhaoran Wang, Han Liu