This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

Given a separation oracle for a convex set K⊂ ℝ n that is contained in a box of radius R, the
goal is to either compute a point in K or prove that K does not contain a ball of radius є. We …

Kernel methods are fundamental tools in machine learning that allow detection of non-linear
dependencies between data without explicitly constructing feature vectors in high …

Acoustic eavesdrop** on private or confidential spaces poses a significant privacy threat.
While soundproof rooms can mitigate these risks to some extent, they are unable to counter …

We study active learning methods for single index models of the form $ F ({\bm x})= f (⟨{\bm
w},{\bm x}⟩) $, where $ f:\mathbb {R}\to\mathbb {R} $ and ${\bx,\bm w}\in\mathbb {R}^ d $. In …

We introduce a general framework for active learning in regression problems. Our
framework extends the standard setup by allowing for general types of data, rather than …

We study active sampling algorithms for linear regression, which aim to query only a small
number of entries of a target vector and output a near minimizer to the objective function. For …

Leverage score sampling is a powerful technique that originates from theoretical computer
science, which can be used to speed up a large number of fundamental questions, eg linear …

Matrix sensing is a problem in signal processing and machine learning that involves
recovering a low-rank matrix from a set of linear measurements. The goal is to reconstruct …