34 citations · 88 across the 4 of their papers we have counts for
4 papers
A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors
Wen Huang, Paul Hand, Reinhard Heckel +1
Deep generative modeling has led to new and state of the art approaches for enforcing structural priors in a variety of inverse problems. In contrast to priors given by sparsity, d…
Corruption Robust Phase Retrieval via Linear Programming
Paul Hand, Vladislav Voroninski
We consider the problem of phase retrieval from corrupted magnitude observations. In particular we show that a fixed can be recovered exactly from corrupted…
An Elementary Proof of Convex Phase Retrieval in the Natural Parameter Space via the Linear Program PhaseMax
Paul Hand, Vladislav Voroninski
The phase retrieval problem has garnered significant attention since the development of the PhaseLift algorithm, which is a convex program that operates in a lifted space of matric…
Compressed Sensing from Phaseless Gaussian Measurements via Linear Programming in the Natural Parameter Space
Paul Hand, Vladislav Voroninski
We consider faithfully combining phase retrieval with classical compressed sensing. Inspired by the recent novel formulation for phase retrieval called PhaseMax, we present and ana…