50 citations · 194 across the 41 of their papers we have counts for
6 papers · 1 filter
Accelerated Multi-Time-Scale Stochastic Approximation: Optimal Complexity and Applications in Reinforcement Learning and Multi-Agent Games
Sihan Zeng, Thinh T. Doan
Multi-time-scale stochastic approximation is an iterative algorithm for finding the fixed point of a set of coupled operators given their noisy samples. It has been observed th…
Resilient Two-Time-Scale Local Stochastic Gradient Descent for Byzantine Federated Learning
Amit Dutta, Thinh T. Doan
We study local stochastic gradient descent methods for solving federated optimization over a network of agents communicating indirectly through a centralized coordinator. We are in…
Bayesian meta learning for trustworthy uncertainty quantification
Zhenyuan Yuan, Thinh T. Doan
We consider the problem of Bayesian regression with trustworthy uncertainty quantification. We define that the uncertainty quantification is trustworthy if the ground truth can be…
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves multiple tasks at the same time. This paper presents a constrained formulation for multi…
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning
Sihan Zeng, Thinh T. Doan
Two-time-scale optimization is a framework introduced in Zeng et al. (2024) that abstracts a range of policy evaluation and policy optimization problems in reinforcement learning (…
Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving Finite-Sample Complexity
Thinh T. Doan
This paper proposes to develop a new variant of the two-time-scale stochastic approximation to find the roots of two coupled nonlinear operators, assuming only noisy samples of the…