Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

This paper studies the fair range clustering problem in which the data points are from
different demographic groups and the goal is to pick $ k $ centers with the minimum …

We consider the $ k $-clustering problem with $\ell_p $-norm cost, which includes $ k $-
median, $ k $-means and $ k $-center, under an individual notion of fairness proposed by …

Multivariate time series (MTS) forecasting penetrates various aspects of our economy and
society, whose roles become increasingly recognized. However, often MTS forecasting is …

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 proportional clustering problem of Chen et al.(ICML'19) and relate it to the area
of multiwinner voting in computational social choice. We show that any clustering satisfying …

Individual preference (IP) stability, introduced by Ahmadi et al.(ICML 2022), is a natural
clustering objective inspired by stability and fairness constraints. A clustering is $\alpha $-IP …

Many clustering algorithms are guided by certain cost functions such as the widely-used k-
means cost. These algorithms divide data points into clusters with often complicated …

In this paper, we propose a natural notion of individual preference (IP) stability for clustering,
which asks that every data point, on average, is closer to the points in its own cluster than to …

Abstract The classic Cluster Editing problem (also known as Correlation Clustering) asks to
transform a given graph into a disjoint union of cliques (clusters) by a small number of edge …