Compressive sensing (CS) states that a sparse signal can be recovered from a small
number of linear measurements, and that this recovery can be performed efficiently in …

Group-based sparsity models are instrumental in linear and non-linear regression problems.
The main premise of these models is the recovery of “interpretable” signals through the …

For every fixed constant α> 0, we design an algorithm for computing the k-sparse Walsh-
Hadamard transform (ie, Discrete Fourier Transform over the Boolean cube) of an N …

Sparsity has become an important tool in many mathematical sciences such as statistics,
machine learning, and signal processing. While sparsity is a good model for data in many …

The problem of approximately computing the k dominant Fourier coefficients of a vector X
quickly, and using few samples in time domain, is known as the Sparse Fourier Transform …

Compressive sensing is a method for recording ak-sparse signal x∈ ℝ n with (possibly
noisy) linear measurements of the form y= Ax, where A∈ ℝ m× n describes the …

During the past decades, sparsity has been shown to be of significant importance in fields
such as compression, signal sampling and analysis, machine learning, and optimization. In …

We revisit the asymptotic analysis of probabilistic construction of adjacency matrices of
expander graphs proposed in Bah and Tanner. With better bounds we derived a new …

In this article we study the problem of signal recovery for group models. More precisely for a
given set of groups, each containing a small subset of indices, and for given linear sketches …

Many success stories in the data sciences share an intriguing computational phenomenon.
While the core algorithmic problems might seem intractable at first, simple heuristics or …