Chris Junchi Li

According to our database1, Chris Junchi Li authored at least 33 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize.
CoRR, 2024

Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates.
CoRR, 2024

Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization.
CoRR, 2024

A General Continuous-Time Formulation of Stochastic ADMM and Its Variants.
CoRR, 2024

2023
Accelerating Inexact HyperGradient Descent for Bilevel Optimization.
CoRR, 2023

Nonconvex stochastic scaled gradient descent and generalized eigenvector problems.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization.
Proceedings of the International Conference on Machine Learning, 2023

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization.
CoRR, 2022

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning.
CoRR, 2022

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium.
CoRR, 2022

Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization.
CoRR, 2022

Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging.
CoRR, 2021

Stochastic Approximation for Online Tensorial Independent Component Analysis.
Proceedings of the Conference on Learning Theory, 2021

2020
Stochastic Modified Equations for Continuous Limit of Stochastic ADMM.
CoRR, 2020

On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration.
Proceedings of the Conference on Learning Theory, 2020

2019
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory.
Proceedings of the 36th International Conference on Machine Learning, 2019

On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
Near-optimal stochastic approximation for online principal component estimation.
Math. Program., 2018

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack.
CoRR, 2018

Diffusion Approximations for Online Principal Component Estimation and Global Convergence.
CoRR, 2018

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Statistical Sparse Online Regression: A Diffusion Approximation Perspective.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent.
CoRR, 2017

Online Multiview Representation Learning: Dropping Convexity for Better Efficiency.
CoRR, 2017

Diffusion Approximations for Online Principal Component Estimation and Global Convergence.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016


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