8 citations · 13 across the 3 of their papers we have counts for
5 papers
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…
Stochastic Recursive Momentum Method for Non-Convex Compositional Optimization
Huizhuo Yuan, Wenqing Hu
We propose a novel stochastic optimization algorithm called STOchastic Recursive Momentum for Compositional (STORM-Compositional) optimization that minimizes the composition of exp…
Stochastic Recursive Momentum for Policy Gradient Methods
Huizhuo Yuan, Xiangru Lian, Ji Liu +1
In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced po…
Stochastic Modified Equations for Continuous Limit of Stochastic ADMM
Xiang Zhou, Huizhuo Yuan, Chris Junchi Li +1
Stochastic version of alternating direction method of multiplier (ADMM) and its variants (linearized ADMM, gradient-based ADMM) plays a key role for modern large scale machine lear…
Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization
Huizhuo Yuan, Xiangru Lian, Ji Liu
Stochastic compositional optimization arises in many important machine learning tasks such as value function evaluation in reinforcement learning and portfolio management. The obje…