Chris Junchi Li

40 papers A* 11A 3Journal 23Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Hessian-Aware Zeroth-Order Optimization. IEEE Trans. Pattern Anal. Mach. Intell. Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
2024	J	`jnl`	A General Continuous-Time Formulation of Stochastic ADMM and Its Variants. CoRR Chris Junchi Li
2024	J	`jnl`	Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization. CoRR Chris Junchi Li
2024	J	`jnl`	Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize. CoRR Tong Zhang, Chris Junchi Li
2024	J	`jnl`	Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates. CoRR Chris Junchi Li
2023	A*	`conf`	A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. ICLR Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan
2023	J	`jnl`	Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan
2023	A*	`conf`	Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan
2023	A	`conf`	Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI Chris Junchi Li, Michael I. Jordan
2023	A*	`conf`	Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. NeurIPS Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du
2022	J	`jnl`	A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan
2022	A*	`conf`	Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022	J	`jnl`	Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022	J	`jnl`	Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization. CoRR Chris Junchi Li, Angela Yuan, Gauthier Gidel, Michael I. Jordan
2022	A	`conf`	On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2022	J	`jnl`	Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. CoRR Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li
2022	A*	`conf`	ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. COLT Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan
2021	J	`jnl`	Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems. CoRR Chris Junchi Li, Michael I. Jordan
2021	J	`jnl`	On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2021	A*	`conf`	Stochastic Approximation for Online Tensorial Independent Component Analysis. COLT Chris Junchi Li, Michael I. Jordan
2020	A*	`conf`	On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. CoRR Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. CoRR Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan
2020	J	`jnl`	Stochastic Approximation for Online Tensorial Independent Component Analysis. CoRR Chris Junchi Li, Michael I. Jordan
2020	J	`jnl`	Stochastic Modified Equations for Continuous Limit of Stochastic ADMM. CoRR Xiang Zhou, Huizhuo Yuan, Chris Junchi Li, Qingyun Sun
2019	A*	`conf`	Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory. ICML Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun
2019	A*	`conf`	Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. NeurIPS Huizhuo Yuan, Xiangru Lian, Chris Junchi Li, Ji Liu, Wenqing Hu
2019	—	`conf`	On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization. IEEE BigData Wenqing Hu, Chris Junchi Li, Xiang Zhou
2018	J	`jnl`	Diffusion Approximations for Online Principal Component Estimation and Global Convergence. CoRR Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang
2018	J	`jnl`	Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. CoRR Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
2018	J	`jnl`	Near-optimal stochastic approximation for online principal component estimation. Math. Program. Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang
2018	J	`jnl`	Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. CoRR Chris Junchi Li, Zhaoran Wang, Han Liu
2018	J	`jnl`	SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator. CoRR Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
2018	A*	`conf`	SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. NeurIPS Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
2018	A	`conf`	Statistical Sparse Online Regression: A Diffusion Approximation Perspective. AISTATS Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun
2017	J	`jnl`	Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent. CoRR Chris Junchi Li, Lei Li, Junyang Qian, Jian-Guo Liu
2017	—	`conf`	Diffusion Approximations for Online Principal Component Estimation and Global Convergence. NIPS Chris Junchi Li, Mengdi Wang, Tong Zhang
2017	J	`jnl`	Online Multiview Representation Learning: Dropping Convexity for Better Efficiency. CoRR Zhehui Chen, Forest L. Yang, Chris Junchi Li, Tuo Zhao
2017	A*	`conf`	Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. ICML Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao
2016	—	`conf`	Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. NIPS Chris Junchi Li, Zhaoran Wang, Han Liu