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 consider the problem of nonadaptive noiseless group testing of N items of which K are
defective. We describe four detection algorithms, the COMP algorithm of Chan et al., two …

This paper proposes a deep learning approach to the adaptive and sequential beamforming
design problem for the initial access phase in a mmWave environment with a single-path …

Learning parameters from voluminous data can be prohibitive in terms of memory and
computational requirements. We propose a 'compressive learning'framework, where we …

We study the problem of detection of ap-dimensional sparse vector of parameters in the
linear regression model with Gaussian noise. We establish the detection boundary, ie, the …

We reconsider the ranking and selection (R&S) problem in stochastic simulation
optimization in light of high-performance, parallel computing, where we take “R&S” to mean …

Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as
follow up of patients with tumors and therapy response assessment. In this paper, the …

Maximizing high-dimensional, non-convex functions through noisy observations is a
notoriously hard problem, but one that arises in many applications. In this paper, we tackle …

We define capacity for group testing problems and deduce bounds for the capacity of a
variety of noisy models, based on the capacity of equivalent noisy communication channels …

Two-sample multiple testing has a wide range of applications. The conventional practice first
reduces the original observations to a vector of p-values and then chooses a cut-off to adjust …