%AMakarychev, Yury%AVakilian, Ali%ABelkin, Mikhail Ed.%AKpotufe, Samor Ed.%BJournal Name: Proceedings of the Conference on Learning Theory, PMLR; Journal Volume: 134
%D2021%I
%JJournal Name: Proceedings of the Conference on Learning Theory, PMLR; Journal Volume: 134
%K
%MOSTI ID: 10336944
%PMedium: X; Size: 3246-3264
%TApproximation Algorithms for Socially Fair Clustering
%XWe present an $e^{O(p)} (\log \ell) / (\log \log \ell)$-approximation algorithm for socially fair clustering with the $\ell_p$-objective. In this problem, we are given a set of points in a metric space. Each point belongs to one (or several) of $\ell$ groups. The goal is to find a $k$-medians, $k$-means, or, more generally, $\ell_p$-clustering that is simultaneously good for all of the groups. More precisely, we need to find a set of $k$ centers $C$ so as to minimize the maximum over all groups $j$ of $\sum_{u \text{ in group } j} d(u, C)^p$. The socially fair clustering problem was independently proposed by Abbasi, Bhaskara, and Venkatasubramanian (2021) and Ghadiri, Samadi, and Vempala (2021). Our algorithm improves and generalizes their $O(\ell)$-approximation algorithms for the problem. The natural LP relaxation for the problem has an integrality gap of $\Omega(\ell)$. In order to obtain our result, we introduce a strengthened LP relaxation and show that it has an integrality gap of $\Theta((\log \ell) / (\log \log \ell))$ for a fixed p. Additionally, we present a bicriteria approximation algorithm, which generalizes the bicriteria approximation of Abbasi et al. (2021).
%0Journal Article
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