55 citations · 79 across the 10 of their papers we have counts for
7 papers · 1 filter
Data Sampling Affects the Complexity of Online SGD over Dependent Data
Shaocong Ma, Ziyi Chen, Yi Zhou +2
Conventional machine learning applications typically assume that data samples are independently and identically distributed (i.i.d.). However, practical scenarios often involve a d…
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization
Ziyi Chen, Bhavya Kailkhura, Yi Zhou
Many important machine learning applications involve regularized nonconvex bi-level optimization. However, the existing gradient-based bi-level optimization algorithms cannot handl…
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…
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…
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
Cheng Chen, Ziyi Chen, Yi Zhou +1
We develop FedCluster--a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties. The FedCluster groups the…