294 citations · 958 across the 32 of their papers we have counts for
21 papers · 1 filter
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…
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…
Beyond Independent Measurements: General Compressed Sensing with GNN Application
Alireza Naderi, Yaniv Plan
We consider the problem of recovering a structured signal from noisy linear observations . The measure…
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate
Michael P. Friedlander, Halyun Jeong, Yaniv Plan +1
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence. Ther…
On the best choice of Lasso program given data parameters
Aaron Berk, Yaniv Plan, Özgür Yilmaz
Generalized compressed sensing (GCS) is a paradigm in which a structured high-dimensional signal may be recovered from random, under-determined, and corrupted linear measurements.…