16 citations · 36 across the 7 of their papers we have counts for
7 papers
An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization
Deyi Liu, Lam M. Nguyen, Quoc Tran-Dinh
In this note we propose a new variant of the hybrid variance-reduced proximal gradient method in [7] to solve a common stochastic composite nonconvex optimization problem under sta…
Robust and Generalizable Visual Representation Learning via Random Convolutions
Zhenlin Xu, Deyi Liu, Junlin Yang +2
While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In…
Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems
Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen
We develop a novel and single-loop variance-reduced algorithm to solve a class of stochastic nonconvex-convex minimax problems involving a nonconvex-linear objective function, whic…
A New Primal-Dual Algorithm for a Class of Nonlinear Compositional Convex Optimization Problems
Yuzixuan Zhu, Deyi Liu, Quoc Tran-Dinh
We develop a novel primal-dual algorithm to solve a class of nonsmooth and nonlinear compositional convex minimization problems, which covers many existing and brand-new models as…
A New Randomized Primal-Dual Algorithm for Convex Optimization with Optimal Last Iterate Rates
Quoc Tran-Dinh, Deyi Liu
We develop a novel unified randomized block-coordinate primal-dual algorithm to solve a class of nonsmooth constrained convex optimization problems, which covers different existing…
A Newton Frank-Wolfe Method for Constrained Self-Concordant Minimization
Deyi Liu, Volkan Cevher, Quoc Tran-Dinh
We demonstrate how to scalably solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO) over the constraint set. We prove that the…
An Inexact Interior-Point Lagrangian Decomposition Algorithm with Inexact Oracles
Deyi Liu, Quoc Tran-Dinh
We develop a new inexact interior-point Lagrangian decomposition method to solve a wide range class of constrained composite convex optimization problems. Our method relies on four…