50 citations · 63 across the 5 of their papers we have counts for
8 papers
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…
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness
Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham +1
Motivated by broad applications in reinforcement learning and machine learning, this paper considers the popular stochastic gradient descent (SGD) when the gradients of the underly…
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation
Thinh T. Doan
Motivated by their broad applications in reinforcement learning, we study the linear two-time-scale stochastic approximation, an iterative method using two different step sizes for…
Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation
Thinh T. Doan, Justin Romberg
Two-time-scale stochastic approximation is a popular iterative method for finding the solution of a system of two equations. Such methods have found broad applications in many area…
A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors
Pietro Pierpaoli, Thinh T. Doan, Justin Romberg +1
Given a list of behaviors and associated parameterized controllers for solving different individual tasks, we study the problem of selecting an optimal sequence of coordinated beha…