353 citations · 1k across the 59 of their papers we have counts for
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A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems
Junqi Tang, Mike Davies
In this work we propose an efficient stochastic plug-and-play (PnP) algorithm for imaging inverse problems. The PnP stochastic gradient descent methods have been recently proposed…
SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization
Derek Driggs, Junqi Tang, Jingwei Liang +2
We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems. Large-scale i…
The Practicality of Stochastic Optimization in Imaging Inverse Problems
Junqi Tang, Karen Egiazarian, Mohammad Golbabaee +1
In this work we investigate the practicality of stochastic gradient descent and recently introduced variants with variance-reduction techniques in imaging inverse problems. Such al…
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes
Junqi Tang, Mohammad Golbabaee, Francis Bach +1
We propose a structure-adaptive variant of the state-of-the-art stochastic variance-reduced gradient algorithm Katyusha for regularized empirical risk minimization. The proposed me…
Structure-Adaptive, Variance-Reduced, and Accelerated Stochastic Optimization
Junqi Tang, Francis Bach, Mohammad Golbabaee +1
In this work we explore the fundamental structure-adaptiveness of state of the art randomized first order algorithms on regularized empirical risk minimization tasks, where the sol…
Exploiting the Structure via Sketched Gradient Algorithms
Junqi Tang, Mohammad Golbabaee, Mike Davies
Sketched gradient algorithms have been recently introduced for efficiently solving the large-scale constrained Least-squares regressions. In this paper we provide novel convergence…