5 accepted papers
In this paper, we examine the problem of locating vector outliers from a large number of inliers, with a particular focus on the case where the outliers are represented in a known basis or dictionary. Using a convex demixing formulation, we provide provable guarantees for exact recovery of the space…
This paper examines the existence of efficiently implementable approximations of a general real linear dimensionality reduction (LDR) operator. The specific focus is on approximating a given LDR operator with a partial circulant structured matrix (a matrix whose rows are related by circular shifts)…
This paper considers the problem of estimating an unknown high dimensional signal from (typically low-dimensional) noisy linear measurements, where the desired unknown signal is assumed to possess a group-sparse structure, i.e. given a (pre-defined) partition of its entries into groups, only a small…
We study the problem of actively learning a multi-index function of the form f(x) = g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> x) fr…
This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance. Our results show that accurate outlier identification is achievable using…