50 citations · 194 across the 41 of their papers we have counts for
9 papers · 1 filter
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance
Thinh T. Doan
Two-time-scale stochastic approximation, a generalized version of the popular stochastic approximation, has found broad applications in many areas including stochastic control, opt…
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
We study a decentralized variant of stochastic approximation, a data-driven approach for finding the root of an operator under noisy measurements. A network of agents, each with it…
Distributed two-time-scale methods over clustered networks
Thiem V. Pham, Thinh T. Doan, Dinh Hoa Nguyen
In this paper, we consider consensus problems over a network of nodes, where the network is divided into a number of clusters. We are interested in the case where the communication…
Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy
Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya
This paper considers the problem of Byzantine fault-tolerance in multi-agent decentralized optimization. In this problem, each agent has a local cost function. The goal of a decent…
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms
Thinh T. Doan
Motivated by broad applications in reinforcement learning and federated learning, we study local stochastic approximation over a network of agents, where their goal is to find the…
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning
Sihan Zeng, Aqeel Anwar, Thinh Doan +2
We develop a mathematical framework for solving multi-task reinforcement learning (MTRL) problems based on a type of policy gradient method. The goal in MTRL is to learn a common p…