42 citations · 52 across the 3 of their papers we have counts for
9 papers
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan +1
We develop two new algorithms, called, FedDR and asyncFedDR, for solving a fundamental nonconvex composite optimization problem in federated learning. Our algorithms rely on a nove…
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…
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan +3
We propose a novel hybrid stochastic policy gradient estimator by combining an unbiased policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted SARA…
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Quoc Tran-Dinh, Nhan H. Pham, Lam M. Nguyen
We develop two new stochastic Gauss-Newton algorithms for solving a class of non-convex stochastic compositional optimization problems frequently arising in practice. We consider b…
Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning
Thinh T. Doan, Lam M. Nguyen, Nhan H. Pham +1
Motivated by broad applications in machine learning, we study the popular accelerated stochastic gradient descent (ASGD) algorithm for solving (possibly nonconvex) optimization pro…
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan +1
We introduce a new approach to develop stochastic optimization algorithms for a class of stochastic composite and possibly nonconvex optimization problems. The main idea is to comb…