27 citations · 52 across the 6 of their papers we have counts for
8 papers
Nested Bregman Iterations for Decomposition Problems
Tobias Wolf, Derek Driggs, Kostas Papafitsoros +2
We consider the task of image reconstruction while simultaneously decomposing the reconstructed image into components with different features. A commonly used tool for this is a va…
Practical Acceleration of the Condat-Vũ Algorithm
Derek Driggs, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb +1
The Condat-Vũ algorithm is a widely used primal-dual method for optimizing composite objectives of three functions. Several algorithms for optimizing composite objectives of two fu…
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…
Accelerating Variance-Reduced Stochastic Gradient Methods
Derek Driggs, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb
Variance reduction is a crucial tool for improving the slow convergence of stochastic gradient descent. Only a few variance-reduced methods, however, have yet been shown to directl…
On Biased Stochastic Gradient Estimation
Derek Driggs, Jingwei Liang, Carola-Bibiane Schönlieb
We present a uniform analysis of biased stochastic gradient methods for minimizing convex, strongly convex, and non-convex composite objectives, and identify settings where bias is…
Tensor Robust Principal Component Analysis: Better recovery with atomic norm regularization
Derek Driggs, Stephen Becker, Jordan Boyd-Graber
This paper studies tensor-based Robust Principal Component Analysis (RPCA) using atomic-norm regularization. Given the superposition of a sparse and a low-rank tensor, we present c…