We design replicable algorithms in the context of statistical clustering under the recently
introduced notion of replicability from Impagliazzo et al.[2022]. According to this definition, a …

The Uncapacitated Facility Location (UFL) problem is one of the most fundamental
clustering problems: Given a set of clients C and a set of facilities F in a metric space (C∪ F …

Abstract The $ k $-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the
practitioners' choice algorithm for optimizing the popular $ k $-means clustering objective …

We present a scalable algorithm for the individually fair ($ p $, $ k $)-clustering problem
introduced by Jung et al. and Mahabadi et al. Given $ n $ points $ P $ in a metric space, let …

We study the differentially private (DP) $ k $-means and $ k $-median clustering problems of
$ n $ points in $ d $-dimensional Euclidean space in the massively parallel computation …

We show that the RandomCoordinateCut algorithm gives the optimal competitive ratio for
explainable $ k $-medians in $\ell_1 $. The problem of explainable $ k $-medians was …

Fair facility location problems try to balance access costs to open facilities borne by different
groups of people by minimizing the $ L_p $ norm of these group distances. However, there …

We consider the well-studied Robust $(k, z) $-Clustering problem, which generalizes the
classic $ k $-Median, $ k $-Means, and $ k $-Center problems. Given a constant $ z\ge 1 …

We study the problem of resilient clustering in the metric setting where one is interested in
designing algorithms that return high quality solutions that preserve the clustering structure …

As a major unsupervised learning method, clustering has received a lot of attention over
multiple decades. The various clustering problems that have been studied intensively …