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 …

This paper considers the well-studied algorithmic regime of designing a (1+ϵ)-
approximation algorithm for a k-clustering problem that runs in time f(k,ϵ)poly(n) (sometimes …

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 …

In this work, we study the socially fair k-median/k-means problem. We are given a set of
points P in a metric space X with a distance function d (.,.). There are ℓ groups: P 1,…, P ℓ⊆ …

Ranking algorithms find extensive usage in diverse areas such as web search, employment,
college admission, voting, etc. The related rank aggregation problem deals with combining …

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 …

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 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 …

Low-rank approximation and column subset selection are two fundamental and related
problems that are applied across a wealth of machine learning applications. In this paper …