Chris Junchi Li | DeepAI

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∙ 06/30/2023

Accelerating Inexact HyperGradient Descent for Bilevel Optimization

We present a method for solving general nonconvex-strongly-convex bileve...

0 Haikuo Yang, et al. ∙

∙ 10/31/2022

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

We provide a novel first-order optimization algorithm for bilinearly-cou...

0 Chris Junchi Li, et al. ∙

∙ 09/30/2022

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning

With the increasing need for handling large state and action spaces, gen...

13 Zixiang Chen, et al. ∙

∙ 08/10/2022

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium

We consider learning Nash equilibria in two-player zero-sum Markov Games...

11 Chris Junchi Li, et al. ∙

∙ 06/17/2022

Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization

We consider the smooth convex-concave bilinearly-coupled saddle-point pr...

0 Simon S. Du, et al. ∙

∙ 12/29/2021

Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems

Motivated by the problem of online canonical correlation analysis, we pr...

6 Chris Junchi Li, et al. ∙

∙ 06/30/2021

On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging

We study the stochastic bilinear minimax optimization problem, presentin...

1 Chris Junchi Li, et al. ∙

∙ 12/28/2020

Stochastic Approximation for Online Tensorial Independent Component Analysis

Independent component analysis (ICA) has been a popular dimension reduct...

0 Chris Junchi Li, et al. ∙

∙ 08/28/2020

ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm

The theory and practice of stochastic optimization has focused on stocha...

19 Chris Junchi Li, et al. ∙

∙ 04/09/2020

On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration

We undertake a precise study of the asymptotic and non-asymptotic proper...

3 Wenlong Mou, et al. ∙

∙ 03/07/2020

Stochastic Modified Equations for Continuous Limit of Stochastic ADMM

Stochastic version of alternating direction method of multiplier (ADMM) ...

0 Xiang Zhou, et al. ∙

∙ 12/29/2018

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack

Zeroth-order optimization or derivative-free optimization is an importan...

0 Haishan Ye, et al. ∙

∙ 09/06/2018

A note on concentration inequality for vector-valued martingales with weak exponential-type tails

We present novel martingale concentration inequalities for martingale di...

0 Chris Junchi Li, et al. ∙

∙ 08/29/2018

Diffusion Approximations for Online Principal Component Estimation and Global Convergence

In this paper, we propose to adopt the diffusion approximation tools to ...

4 Chris Junchi Li, et al. ∙

∙ 08/29/2018

Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

Solving statistical learning problems often involves nonconvex optimizat...

2 Chris Junchi Li, et al. ∙

∙ 07/04/2018

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator

In this paper, we propose a new technique named Stochastic Path-Integrat...

0 Cong Fang, et al. ∙

∙ 09/02/2017

A convergence analysis of the perturbed compositional gradient flow: averaging principle and normal deviations

We consider in this work a system of two stochastic differential equatio...

0 Wenqing Hu, et al. ∙

∙ 05/22/2017

Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent

In this paper, we study the stochastic gradient descent method in analyz...

0 Chris Junchi Li, et al. ∙

∙ 03/16/2016

Near-Optimal Stochastic Approximation for Online Principal Component Estimation

Principal component analysis (PCA) has been a prominent tool for high-di...

0 Chris Junchi Li, et al. ∙

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