18 citations · 18 across the 1 of their papers we have counts for
5 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…
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…
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems
Thinh T. Doan
There are much recent interests in solving noncovnex min-max optimization problems due to its broad applications in many areas including machine learning, networked resource alloca…
On the geometric convergence rate of distributed economic dispatch/demand response in power networks
Thinh T. Doan, Alex Olshevsky
Motivated by potential applications in power systems, we study a problem of optimizing a sum of convex functions on dynamic networks of nodes when each function is known to…