Chris Junchi Li

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 39

Top Topics

Diffusion Processes

Convex Optimization

Top Venues

Publications

Tong Zhang, Chris Junchi Li
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize. CoRR (2024)
Chris Junchi Li
Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates. CoRR (2024)
Chris Junchi Li
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization. CoRR (2024)
Chris Junchi Li
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants. CoRR (2024)
Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML (2023)
Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. NeurIPS (2023)
Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. ICLR (2023)
Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR (2023)
Chris Junchi Li, Michael I. Jordan
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI (2023)
Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR (2022)
Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. COLT (2022)
Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR (2022)
Chris Junchi Li, Angela Yuan, Gauthier Gidel, Michael I. Jordan
Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization. CoRR (2022)
Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. CoRR (2022)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS (2022)
Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS (2022)
Chris Junchi Li, Michael I. Jordan
Stochastic Approximation for Online Tensorial Independent Component Analysis. COLT (2021)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR (2021)
Chris Junchi Li, Michael I. Jordan
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems. CoRR (2021)
Chris Junchi Li, Michael I. Jordan
Stochastic Approximation for Online Tensorial Independent Component Analysis. CoRR (2020)
Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. CoRR (2020)
Xiang Zhou, Huizhuo Yuan, Chris Junchi Li, Qingyun Sun
Stochastic Modified Equations for Continuous Limit of Stochastic ADMM. CoRR (2020)
Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. CoRR (2020)
Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT (2020)
Wenqing Hu, Chris Junchi Li, Xiang Zhou
On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization. IEEE BigData (2019)
Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory. ICML (2019)
Huizhuo Yuan, Xiangru Lian, Chris Junchi Li, Ji Liu, Wenqing Hu
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. NeurIPS (2019)
Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang
Near-optimal stochastic approximation for online principal component estimation. Math. Program. 167 (1) (2018)
Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. CoRR (2018)
Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun
Statistical Sparse Online Regression: A Diffusion Approximation Perspective. AISTATS (2018)
Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. CoRR (2018)
Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator. CoRR (2018)
Chris Junchi Li, Zhaoran Wang, Han Liu
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. CoRR (2018)
Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. NeurIPS (2018)
Zhehui Chen, Forest L. Yang, Chris Junchi Li, Tuo Zhao
Online Multiview Representation Learning: Dropping Convexity for Better Efficiency. CoRR (2017)
Chris Junchi Li, Lei Li, Junyang Qian, Jian-Guo Liu
Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent. CoRR (2017)
Chris Junchi Li, Mengdi Wang, Tong Zhang
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. NIPS (2017)
Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. ICML (2017)
Chris Junchi Li, Zhaoran Wang, Han Liu
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. NIPS (2016)