55 citations · 79 across the 10 of their papers we have counts for
6 papers · 1 filter
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning
Ziyi Chen, Shaocong Ma, Yi Zhou
Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm that has been widely used for model training in various machine learning applications, which aims to solve…
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization
Ziyi Chen, Zhengyang Hu, Qunwei Li +2
Gradient descent-ascent (GDA) is a widely used algorithm for minimax optimization. However, GDA has been proved to converge to stationary points for nonconvex minimax optimization,…
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis
Ziyi Chen, Yi Zhou, Rongrong Chen +1
Actor-critic (AC) algorithms have been widely adopted in decentralized multi-agent systems to learn the optimal joint control policy. However, existing decentralized AC algorithms…
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
Shaocong Ma, Ziyi Chen, Yi Zhou +1
Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control. Recently, the finite-time analysis of Greedy-GQ has been developed under linear function appro…
Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity
Ziyi Chen, Yi Zhou, Rongrong Chen
The finite-time convergence of off-policy TD learning has been comprehensively studied recently. However, such a type of convergence has not been well established for off-policy TD…
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry
Ziyi Chen, Yi Zhou, Tengyu Xu +1
The gradient descent-ascent (GDA) algorithm has been widely applied to solve minimax optimization problems. In order to achieve convergent policy parameters for minimax optimizatio…