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 …

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 …

Clustering is a fundamental unsupervised learning problem where a dataset is partitioned
into clusters that consist of nearby points in a metric space. A recent variant, fair clustering …

Clustering is a fundamental problem in unsupervised machine learning, and due to its
numerous societal implications fair variants of it have recently received significant attention …

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 …

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 …

Electroencephalogram (EEG)-based applications in Brain-Computer Interfaces (BCIs, or
Human-Machine Interfaces, HMIs), diagnosis of neurological disease, rehabilitation, etc, rely …

Increasing adoption of online commerce has created income opportunities for millions of
delivery drivers who deliver items, from clothes and smartphones to foods and medicines, to …

We revisit the problem of fair clustering, first introduced by Chierichetti et al.(Fair clustering
through fairlets, 2017), which requires each protected attribute to have approximately equal …

In this paper, we study the individual preference (IP) stability, which is an notion capturing
individual fairness and stability in clustering. Within this setting, a clustering is $\alpha $-IP …