Given a set of $ n $ points in $ d $ dimensions, the Euclidean $ k $-means problem (resp.
Euclidean $ k $-median) consists of finding $ k $ centers such that the sum of squared …

The (k, z)-clustering problem consists of finding a set of k points called centers, such that the
sum of distances raised to the power of z of every data point to its closest center is …

Motivated by practical generalizations of the classic k-median and k-means objectives, such
as clustering with size constraints, fair clustering, and Wasserstein barycenter, we introduce …

One-shot coreset selection aims to select a representative subset of the training data, given
a pruning rate, that can later be used to train future models while retaining high accuracy …

In this paper, we consider the problem of finding high dimensional power means: given a set
$ A $ of $ n $ points in $\R^ d $, find the point $ m $ that minimizes the sum of Euclidean …

Subset selection for the rank k approximation of an n× d matrix A offers improvements in the
interpretability of matrices, as well as a variety of computational savings. This problem is well …

Vertical federated learning (VFL), where data features are stored in multiple parties
distributively, is an important area in machine learning. However, the communication …

We consider the classic Euclidean k-median and k-means objective on data streams, where
the goal is to provide a (1+ε)-approximation to the optimal k-median or k-means solution …

Given a set of points of interest, a volumetric spanner is a subset of the points using which all
the points can be expressed using" small" coefficients (measured in an appropriate norm) …

Clustering is an important technique for identifying structural information in large-scale data
analysis, where the underlying dataset may be too large to store. In many applications …