Optimal transportation distances are a fundamental family of parameterized distances for
histograms in the probability simplex. Despite their appealing theoretical properties …

We give algorithms for geometric graph problems in the modern parallel models such as
MapReduce. For example, for the Minimum Spanning Tree (MST) problem over a set of …

We study the problem of learning from data representations that are invariant to
transformations, and at the same time selective, in the sense that two points have the same …

Over the last decade, an immense amount of data has become available. From collections of
photos, to genetic data, and to network traffic statistics, modern technologies and cheap …

A technique introduced by Indyk and Woodruff (STOC 2005) has inspired several recent
advances in data-stream algorithms. We show that a number of these results follow eas-ily …

The data stream model is a fundamental model for processing massive data sets with limited
memory and fast processing time. Recently Hsu et al.(2019) incorporated machine learning …

We show there is a distribution over linear map**s R: l1n-> l1O (d log d), such that with
arbitrarily large constant probability, for any fixed d-dimensional subspace L, for all x∈ L we …

We study streaming algorithms for the fundamental geometric problem of computing the cost
of the Euclidean Minimum Spanning Tree (MST) on an n-point set X⊂ ℝ d. In the streaming …

We study streaming algorithms for two fundamental geometric problems: computing the cost
of a Minimum Spanning Tree (MST) of an n-point set X⊂{1, 2,…, Δ} d, and computing the …

This paper considers the problem of recovering an unknown sparse p× p matrix X from an
m× m matrix Y= AXB T, where A and B are known m× p matrices with m≪ p. The main result …