243 citations · 409 across the 22 of their papers we have counts for
22 papers
Accelerating Inexact HyperGradient Descent for Bilevel Optimization
Haikuo Yang, Luo Luo, Chris Junchi Li +1
We present a method for solving general nonconvex-strongly-convex bilevel optimization problems. Our method -- the \emph{Restarted Accelerated HyperGradient Descent} (\texttt{RAHGD…
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
Chris Junchi Li, Angela Yuan, Gauthier Gidel +2
We propose a new first-order optimization algorithm -- AcceleratedGradient-OptimisticGradient (AG-OG) Descent Ascent -- for separable convex-concave minimax optimization. The main…
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Zixiang Chen, Chris Junchi Li, Angela Yuan +2
With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we pro…
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
Chris Junchi Li, Dongruo Zhou, Quanquan Gu +1
We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a R…
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization
Simon S. Du, Gauthier Gidel, Michael I. Jordan +1
We consider the smooth convex-concave bilinearly-coupled saddle-point problem, , where…
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems
Chris Junchi Li, Michael I. Jordan
Motivated by the problem of online canonical correlation analysis, we propose the \emph{Stochastic Scaled-Gradient Descent} (SSGD) algorithm for minimizing the expectation of a sto…