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Chris Junchi Li
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2020 – today
- 2024
- [i21]Chris Junchi Li:
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants. CoRR abs/2404.14358 (2024) - [i20]Chris Junchi Li:
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization. CoRR abs/2405.00914 (2024) - [i19]Chris Junchi Li:
Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates. CoRR abs/2405.04566 (2024) - [i18]Tong Zhang, Chris Junchi Li:
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize. CoRR abs/2407.10955 (2024) - 2023
- [c17]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 - [c16]Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML 2023: 20351-20383 - [c15]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 - [c14]Chris Junchi Li, Michael I. Jordan:
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI 2023: 1230-1240 - [i17]Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan:
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR abs/2307.00126 (2023) - 2022
- [c13]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: 9793-9826 - [c12]Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan:
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. COLT 2022: 909-981 - [c11]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS 2022 - [i16]Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li:
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. CoRR abs/2206.08573 (2022) - [i15]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR abs/2208.05363 (2022) - [i14]Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR abs/2209.15634 (2022) - [i13]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 abs/2210.17550 (2022) - 2021
- [c10]Chris Junchi Li, Michael I. Jordan:
Stochastic Approximation for Online Tensorial Independent Component Analysis. COLT 2021: 3051-3106 - [i12]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 abs/2107.00464 (2021) - [i11]Chris Junchi Li, Michael I. Jordan:
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems. CoRR abs/2112.14738 (2021) - 2020
- [c9]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: 2947-2997 - [i10]Xiang Zhou, Huizhuo Yuan, Chris Junchi Li, Qingyun Sun:
Stochastic Modified Equations for Continuous Limit of Stochastic ADMM. CoRR abs/2003.03532 (2020) - [i9]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 abs/2004.04719 (2020) - [i8]Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan:
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. CoRR abs/2008.12690 (2020) - [i7]Chris Junchi Li, Michael I. Jordan:
Stochastic Approximation for Online Tensorial Independent Component Analysis. CoRR abs/2012.14415 (2020)
2010 – 2019
- 2019
- [c8]Wenqing Hu, Chris Junchi Li, Xiang Zhou:
On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization. IEEE BigData 2019: 94-104 - [c7]Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun:
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory. ICML 2019: 7232-7241 - [c6]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: 6926-6935 - 2018
- [j1]Chris Junchi Li, Mengdi Wang
, Han Liu, Tong Zhang:
Near-optimal stochastic approximation for online principal component estimation. Math. Program. 167(1): 75-97 (2018) - [c5]Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun:
Statistical Sparse Online Regression: A Diffusion Approximation Perspective. AISTATS 2018: 1017-1026 - [c4]Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang:
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. NeurIPS 2018: 687-697 - [i6]Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang:
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator. CoRR abs/1807.01695 (2018) - [i5]Chris Junchi Li, Zhaoran Wang, Han Liu:
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. CoRR abs/1808.09642 (2018) - [i4]Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. CoRR abs/1808.09645 (2018) - [i3]Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang:
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. CoRR abs/1812.11377 (2018) - 2017
- [c3]Zhehui Chen, Lin F. Yang
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. ICML 2017: 777-786 - [c2]Chris Junchi Li, Mengdi Wang, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. NIPS 2017: 645-655 - [i2]Zhehui Chen, Forest L. Yang, Chris Junchi Li, Tuo Zhao:
Online Multiview Representation Learning: Dropping Convexity for Better Efficiency. CoRR abs/1702.08134 (2017) - [i1]Chris Junchi Li, Lei Li, Junyang Qian, Jian-Guo Liu:
Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent. CoRR abs/1705.07562 (2017) - 2016
- [c1]Chris Junchi Li, Zhaoran Wang, Han Liu:
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. NIPS 2016: 4961-4969
Coauthor Index
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last updated on 2024-08-25 20:05 CEST by the dblp team
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