Fair k-Centers via Maximum Matching

Matthew Jones, Huy Nguyen, Thy Nguyen
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4940-4949, 2020.

Abstract

The field of algorithms has seen a push for fairness, or the removal of inherent bias, in recent history. In data summarization, where a much smaller subset of a data set is chosen to represent the whole of the data, fairness can be introduced by guaranteeing each "demographic group" a specific portion of the representative subset. Specifically, this paper examines this fair variant of the k-centers problem, where a subset of the data with cardinality k is chosen to minimize distance to the rest of the data. Previous papers working on this problem presented both a 3-approximation algorithm with a super-linear runtime and a linear-time algorithm whose approximation factor is exponential in the number of demographic groups. This paper combines the best of each algorithm by presenting a linear-time algorithm with a guaranteed 3-approximation factor and provides empirical evidence of both the algorithm’s runtime and effectiveness.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-jones20a, title = {Fair k-Centers via Maximum Matching}, author = {Jones, Matthew and Nguyen, Huy and Nguyen, Thy}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {4940--4949}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/jones20a/jones20a.pdf}, url = {http://proceedings.mlr.press/v119/jones20a.html}, abstract = {The field of algorithms has seen a push for fairness, or the removal of inherent bias, in recent history. In data summarization, where a much smaller subset of a data set is chosen to represent the whole of the data, fairness can be introduced by guaranteeing each "demographic group" a specific portion of the representative subset. Specifically, this paper examines this fair variant of the k-centers problem, where a subset of the data with cardinality k is chosen to minimize distance to the rest of the data. Previous papers working on this problem presented both a 3-approximation algorithm with a super-linear runtime and a linear-time algorithm whose approximation factor is exponential in the number of demographic groups. This paper combines the best of each algorithm by presenting a linear-time algorithm with a guaranteed 3-approximation factor and provides empirical evidence of both the algorithm’s runtime and effectiveness.} }
Endnote
%0 Conference Paper %T Fair k-Centers via Maximum Matching %A Matthew Jones %A Huy Nguyen %A Thy Nguyen %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-jones20a %I PMLR %P 4940--4949 %U http://proceedings.mlr.press/v119/jones20a.html %V 119 %X The field of algorithms has seen a push for fairness, or the removal of inherent bias, in recent history. In data summarization, where a much smaller subset of a data set is chosen to represent the whole of the data, fairness can be introduced by guaranteeing each "demographic group" a specific portion of the representative subset. Specifically, this paper examines this fair variant of the k-centers problem, where a subset of the data with cardinality k is chosen to minimize distance to the rest of the data. Previous papers working on this problem presented both a 3-approximation algorithm with a super-linear runtime and a linear-time algorithm whose approximation factor is exponential in the number of demographic groups. This paper combines the best of each algorithm by presenting a linear-time algorithm with a guaranteed 3-approximation factor and provides empirical evidence of both the algorithm’s runtime and effectiveness.
APA
Jones, M., Nguyen, H. & Nguyen, T.. (2020). Fair k-Centers via Maximum Matching. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:4940-4949 Available from http://proceedings.mlr.press/v119/jones20a.html.

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