1 citations · 1 across the 6 of their papers we have counts for
6 papers
Convexity from order: a theory of faces and generalized separation
Matthew S. Scott
It is known that any convex cone in a general vector space is the positive cone of a preorder compatible with the vector space, and that any convex set is an affine slice of a conv…
Partially deterministic sampling for compressed sensing with denoising guarantees
Yaniv Plan, Matthew S. Scott, Ozgur Yilmaz
We study compressed sensing when the sampling vectors are chosen from the rows of a unitary matrix. In the literature, these sampling vectors are typically chosen randomly; the use…
Average-case thresholds for exact regularization of linear programs
Michael P. Friedlander, Sharvaj Kubal, Yaniv Plan +1
Small regularizers can preserve linear programming solutions exactly. This paper provides the first average-case analysis of exact regularization: with a standard Gaussian cost vec…
Denoising guarantees for optimized sampling schemes in compressed sensing
Yaniv Plan, Matthew S. Scott, Xia Sheng +1
Compressed sensing with subsampled unitary matrices benefits from \emph{optimized} sampling schemes, which feature improved theoretical guarantees and empirical performance relativ…
Model-adapted Fourier sampling for generative compressed sensing
Aaron Berk, Simone Brugiapaglia, Yaniv Plan +3
We study generative compressed sensing when the measurement matrix is randomly subsampled from a unitary matrix (with the DFT as an important special case). It was recently shown t…
A coherence parameter characterizing generative compressed sensing with Fourier measurements
Aaron Berk, Simone Brugiapaglia, Babhru Joshi +3
In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is…