42 citations · 110 across the 11 of their papers we have counts for
4 papers · 1 filter
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…