42 citations · 157 across the 25 of their papers we have counts for
25 papers
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
Lesi Chen, Boyuan Yao, Luo Luo
This paper considers stochastic first-order algorithms for minimax optimization under Polyak--Łojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem…
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…
Communication Efficient Distributed Newton Method with Fast Convergence Rates
Chengchang Liu, Lesi Chen, Luo Luo +1
We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires communication c…
Symmetric Rank- Methods
Chengchang Liu, Cheng Chen, Luo Luo
This paper proposes a novel class of block quasi-Newton methods for convex optimization which we call symmetric rank- (SR-) methods. Each iteration of SR- incorporates the…
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
Lesi Chen, Jing Xu, Luo Luo
We consider the optimization problem of the form , where the component is -mean-squared Lipschitz but…
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
Lesi Chen, Haishan Ye, Luo Luo
This paper studies the stochastic nonconvex-strongly-concave minimax optimization over a multi-agent network. We propose an efficient algorithm, called Decentralized Recursive grad…