50 citations · 194 across the 41 of their papers we have counts for
10 papers · 1 filter
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…
Convergence Rates of Decentralized Gradient Methods over Cluster Networks
Amit Dutta, Nila Masrourisaadat, Thinh T. Doan
We present an analysis for the performance of decentralized consensus-based gradient (DCG) methods for solving optimization problems over a cluster network of nodes. This type of n…
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes
Sihan Zeng, Thinh T. Doan, Justin Romberg
We consider a discounted cost constrained Markov decision process (CMDP) policy optimization problem, in which an agent seeks to maximize a discounted cumulative reward subject to…
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
We study a new two-time-scale stochastic gradient method for solving optimization problems, where the gradients are computed with the aid of an auxiliary variable under samples gen…
Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy
Nirupam Gupta, Thinh T. Doan, Nitin Vaidya
We consider the problem of Byzantine fault-tolerance in federated machine learning. In this problem, the system comprises multiple agents each with local data, and a trusted centra…
Distributed Dual Subgradient Methods with Averaging and Applications to Grid Optimization
Subhonmesh Bose, Hoa Dinh Nguyen, Haitian Liu +3
We study finite-time performance of a recently proposed distributed dual subgradient (DDSG) method for convex constrained multi-agent optimization problems. The algorithm enjoys pe…