42 citations · 110 across the 11 of their papers we have counts for
6 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…
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…
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
Quoc Tran-Dinh, Nhan H. Pham, Dzung T. Phan +1
We introduce a hybrid stochastic estimator to design stochastic gradient algorithms for solving stochastic optimization problems. Such a hybrid estimator is a convex combination of…
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan +1
We propose a new stochastic first-order algorithmic framework to solve stochastic composite nonconvex optimization problems that covers both finite-sum and expectation settings. Ou…