We introduce a new class of objectives for optimal transport computations of datasets in high-
dimensional Euclidean spaces. The new objectives are parametrized by $\rho\geq 1$, and …

We study efficient mechanisms for differentially private kernel density estimation (DP-KDE).
Prior work for the Gaussian kernel described algorithms that run in time exponential in the …

The distance matrix of a dataset $ X $ of $ n $ points with respect to a distance function $ f $
represents all pairwise distances between points in $ X $ induced by $ f $. Due to their wide …

Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in
machine learning, statistics and other related fields. The main drawback of using kernel …

We study the problem of approximating the eigenspectrum of a symmetric matrix A∈ R n× n
with bounded entries (ie,‖ A‖∞≤ 1). We present a simple sublinear time algorithm that …

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient graph
algorithms. This technical report presents the newly implemented component on locality …

Kernel matrix-vector multiplication (KMVM) is a foundational operation in machine learning
and scientific computing. However, as KMVM tends to scale quadratically in both memory …

A key cog in machine learning is the humble embedding: vector representations of real
world objects such as text, images, graphs, or molecules whose geometric similarities …

Continuous optimization is a fundamental topic both in theoretical computer science and
applications of machine learning. Meanwhile, an important idea in the development modern …

Matrices are ubiquitous mathematical structures that arise throughout computer science. We
study fast algorithms for several central problems involving matrices, including eigenvalue …