Constant-Factor Approximation Algorithms for Socially Fair $k$-ClusteringDownload PDF

Published: 01 Feb 2023, Last Modified: 13 Feb 2023Submitted to ICLR 2023Readers: Everyone
Keywords: Clustering, k-means, fairness, approximation algorithms, iterative rounding
TL;DR: We present a constant-factor approximation algorithm for the socially fair k-means and k-medians problems.
Abstract: We study approximation algorithms for the socially fair $(\ell_p, k)$-clustering problem with $m$ groups which include the socially fair $k$-median ($p=1$) and $k$-means ($p=2$). We present (1) a polynomial-time $(5+2\sqrt{6})^p$-approximation with at most $k+m$ centers (2) a $(5+2\sqrt{6}+\epsilon)^p$-approximation with $k$ centers in time $(nk)^{{2^{O(p)} m^2}/\epsilon}$, and (3) a $(15+6\sqrt{6})^p$ approximation with $k$ centers in time $k^{m}\cdot\text{poly}(n)$. The former is obtained by a refinement of the iterative rounding method via a sequence of linear programs. The latter two are obtained by converting a solution with up to $k+m$ centers to one with $k$ centers by sparsification methods for (2) and via an exhaustive search for (3). We also compare the performance of our algorithms with existing approximation algorithms on benchmark datasets, and find that our algorithms outperform existing methods.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Supplementary Material: zip
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)
11 Replies