In this paper, we develop a new framework for sensing and recovering structured signals. In
contrast to compressive sensing (CS) systems that employ linear measurements, sparse …

This paper proposes a simple adaptive sensing and group testing algorithm for sparse
signal recovery. The algorithm, termed compressive adaptive sense and search (CASS), is …

We study low rank matrix and tensor completion and propose novel algorithms that employ
adaptive sampling schemes to obtain strong performance guarantees for these problems …

In this paper we study the adaptive complexity of submodular optimization. Informally, the
adaptive complexity of a problem is the minimal number of sequential rounds required to …

In this paper we study the adaptivity of submodular maximization. Adaptivity quantifies the
number of sequential rounds that an algorithm makes when function evaluations can be …

Adaptive sampling results in significant improvements in the recovery of sparse signals in
white Gaussian noise. A sequential adaptive sampling-and-refinement procedure called …

Suppose we can sequentially acquire arbitrary linear measurements of an n-dimensional
vector x resulting in the linear model y= A x+ z, where z represents measurement noise. If …

While the conventional compressive sensing assumes measurements of infinite precision,
one-bit compressive sensing considers an extreme setting where each measurement is …

In this paper we consider parallelization for applications whose objective can be expressed
as maximizing a non-monotone submodular function under a cardinality constraint. Our …

This paper considers the problem of identifying the support set of a high-dimensional sparse
vector, from noise-corrupted 1-bit measurements. We present passive and adaptive …