46 citations · 112 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
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…
On oracle-type local recovery guarantees in compressed sensing
Ben Adcock, Claire Boyer, Simone Brugiapaglia
We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing. Our unified analysis based on l1 minimization encompasses the case where…
Robustness to unknown error in sparse regularization
Simone Brugiapaglia, Ben Adcock
Quadratically-constrained basis pursuit has become a popular device in sparse regularization; in particular, in the context of compressed sensing. However, the majority of theoreti…