Many models for sparse regression typically assume that the covariates are known
completely, and without noise. Particularly in high-dimensional applications, this is often not …

The problem of model selection arises in a number of contexts, such as subset selection in
linear regression, estimation of structures in graphical models, and signal denoising. This …

In this paper, we investigate the problem of compressed learning, ie learning directly in the
compressed domain. In particular, we provide tight bounds demonstrating that the linear …

Over the last decade, considerable progress has been made toward develo** new signal
processing methods to manage the deluge of data caused by advances in sensing, imaging …

The central goal of compressed sensing is to capture attributes of a signal using very few
measurements. The initial publications by Donoho and by Candes and Tao have been …

The role of random measurement in compressive sensing is analogous to the role of random
codes in coding theory. In coding theory, decoders that can correct beyond the minimum …

A method for constructing low coherence sensing matrices based on best spherical codes is
proposed. Such matrices are applied in Compressed Sensing (CS) to obtain measurements …

This paper studies the problem of exact localization of multiple objects with noisy data. The
crux of the proposed approach consists of random illumination. Two recovery methods are …

The demand for cybersecurity professionals is projected to grow substantially, with the US
Bureau of Labor Statistics reporting that employment in cybersecurity within the US will grow …

Stationarity of the sparse coefficients as well as the sparseness of their support, along with
incoherence assumptions related to restricted isometry, are fundamental to compressive …