46 citations · 49 across the 5 of their papers we have counts for
5 papers
On the Computational and Statistical Efficiency of the Empirical Maximum Entropy on the Mean Method
Matthew King-Roskamp, Gabriel Rioux, Rustum Choksi +1
The Maximum Entropy on the Mean (MEM) method provides a flexible computational framework for solving inverse problems by combining data fidelity with entropy-based regularization.…
Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse Problems
Matthew King-Roskamp, Rustum Choksi, Tim Hoheisel
We establish the theoretical framework for implementing the maximumn entropy on the mean (MEM) method for linear inverse problems in the setting of approximate (data-driven) priors…
Iterative and greedy algorithms for the sparsity in levels model in compressed sensing
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
Motivated by the question of optimal functional approximation via compressed sensing, we propose generalizations of the Iterative Hard Thresholding and the Compressive Sampling Mat…
The benefits of acting locally: Reconstruction algorithms for sparse in levels signals with stable and robust recovery guarantees
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
The sparsity in levels model recently inspired a new generation of effective acquisition and reconstruction modalities for compressive imaging. Moreover, it naturally arises in var…
Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
A signature result in compressed sensing is that Gaussian random sampling achieves stable and robust recovery of sparse vectors under optimal conditions on the number of measuremen…