On the heels of compressed sensing, a new field has very recently emerged. This field
addresses a broad range of problems of significant practical interest, namely, the recovery of …

High dimensional statistical problems arise from diverse fields of scientific research and
technological development. Variable selection plays a pivotal role in contemporary statistical …

We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized
variable selection in high-dimensional linear regression. The LASSO is fast and continuous …

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear
norm among all matrices obeying a set of convex constraints. This problem may be …

We develop fast algorithms for estimation of generalized linear models with convex
penalties. The models include linear regression, two-class logistic regression, and …

Estimation of structure, such as in variable selection, graphical modelling or cluster analysis,
is notoriously difficult, especially for high dimensional data. We introduce stability selection …

High-rate data communication over a multipath wireless channel often requires that the
channel response be known at the receiver. Training-based methods, which probe the …

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition
of sparse or compressible signals that can be well approximated by just K¿ N elements from …

We consider the problem of estimating the graph associated with a binary Ising Markov
random field. We describe a method based on ℓ 1-regularized logistic regression, in which …

We use Lévy processes to generate joint prior distributions for a location parameter β=(β1,...,
βp) as p grows large. This approach, which generalizes normal scale-mixture priors to an …