Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. - Chris Junchi Li; Zhaoran Wang 001; Han Liu 001 (NIPS, 2016)

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

Diffusion Approximations for Online Principal Component Estimation and Global Convergence. - Chris Junchi Li; Mengdi Wang; Tong Zhang 001 (NIPS, 2017)

Online Multiview Representation Learning: Dropping Convexity for Better Efficiency. - Zhehui Chen; Forest L. Yang; Chris Junchi Li; Tuo Zhao (CoRR, 2017)

Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent. - Chris Junchi Li; Lei Li 006; Junyang Qian; Jian-Guo Liu 006 (CoRR, 2017)

Statistical Sparse Online Regression: A Diffusion Approximation Perspective. - Jianqing Fan; Wenyan Gong; Chris Junchi Li; Qiang Sun 007 (AISTATS, 2018)

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. - Cong Fang 001; Chris Junchi Li; Zhouchen Lin; Tong Zhang 001 (NeurIPS, 2018)

Diffusion Approximations for Online Principal Component Estimation and Global Convergence. - Chris Junchi Li; Mengdi Wang; Han Liu 001; Tong Zhang 001 (CoRR, 2018)

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. - Haishan Ye; Zhichao Huang 002; Cong Fang 001; Chris Junchi Li; Tong Zhang 001 (CoRR, 2018)

Near-optimal stochastic approximation for online principal component estimation. - Chris Junchi Li; Mengdi Wang; Han Liu 001; Tong Zhang 001 (Math. Program., 2018)

On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization. - Wenqing Hu; Chris Junchi Li; Xiang Zhou 001 (IEEE BigData, 2019)

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

Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. - Huizhuo Yuan; Xiangru Lian; Chris Junchi Li; Ji Liu 002; Wenqing Hu (NeurIPS, 2019)

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 (COLT, 2020)

Stochastic Modified Equations for Continuous Limit of Stochastic ADMM. - Xiang Zhou; Huizhuo Yuan; Chris Junchi Li; Qingyun Sun (CoRR, 2020)

Stochastic Approximation for Online Tensorial Independent Component Analysis. - Chris Junchi Li; Michael I. Jordan (COLT, 2021)

On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (CoRR, 2021)

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (AISTATS, 2022)

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

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

Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. - Simon S. Du; Gauthier Gidel; Michael I. Jordan; Chris Junchi Li (CoRR, 2022)

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. - Chris Junchi Li; Dongruo Zhou; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. - Zixiang Chen; Chris Junchi Li; Angela Yuan; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

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

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

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

Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. - Chris Junchi Li; Michael I. Jordan (UAI, 2023)

Accelerating Inexact HyperGradient Descent for Bilevel Optimization. - Haikuo Yang; Luo Luo; Chris Junchi Li; Michael I. Jordan (CoRR, 2023)

A General Continuous-Time Formulation of Stochastic ADMM and Its Variants. - Chris Junchi Li (CoRR, 2024)

Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization. - Chris Junchi Li (CoRR, 2024)

Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates. - Chris Junchi Li (CoRR, 2024)

Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize. - Tong Zhang; Chris Junchi Li (CoRR, 2024)

Hessian-Aware Zeroth-Order Optimization. - Haishan Ye; Zhichao Huang 002; Cong Fang 001; Chris Junchi Li; Tong Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)