4 citations · 6 across the 5 of their papers we have counts for
7 papers · 1 filter
Equivariance Regularization for Image Reconstruction
Junqi Tang
In this work, we propose Regularization-by-Equivariance (REV), a novel structure-adaptive regularization scheme for solving imaging inverse problems under incomplete measurements.…
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…