294 citations · 958 across the 32 of their papers we have counts for
38 papers
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…
STARK denoises spatial transcriptomics images via adaptive regularization
Sharvaj Kubal, Naomi Graham, Matthieu Heitz +4
We present an approach to denoising spatial transcriptomics images that is particularly effective for uncovering cell identities in the regime of ultra-low sequencing depths, and a…
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…
Random matrices acting on sets: Independent columns
Yaniv Plan, Roman Vershynin
We study random matrices with independent subgaussian columns. Assuming each column has a fixed Euclidean norm, we establish conditions under which such matrices act as near-isomet…
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…