In data summarization we want to choose $ k $ prototypes in order to summarize a data set.
We study a setting where the data set comprises several demographic groups and we are …

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 …

In this paper, we present a new iterative rounding framework for many clustering problems.
Using this, we obtain an (α1+ є≤ 7.081+ є)-approximation algorithm for k-median with …

We present an (e^{O (p)}(log\ell)/(log log\ell))-approximation algorithm for socially fair
clustering with the l_p-objective. In this problem, we are given a set of points in a metric …

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 …

Linear programming (LP) has played a key role in the study of algorithms for combinatorial
optimization problems. In the field of approximation algorithms, this is well illustrated by the …

Fueled by massive data, important decision making is being automated with the help of
algorithms, therefore, fairness in algorithms has become an especially important research …

We introduce a novel problem for diversity-aware clustering. We assume that the potential
cluster centers belong to a set of groups defined by protected attributes, such as ethnicity …

In the classic k-center problem, we are given a metric graph, and the objective is to select k
nodes as centers such that the maximum distance from any vertex to its closest center is …

In the mobile facility location problem (MFLP), one seeks to relocate (or move) a set of
existing facilities and assign clients to these facilities so that the sum of facility movement …