AISTATS 2019poster65 citations
Matroids, Matchings, and Fairness
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvtiskii
Abstract
The need for fairness in machine learning algorithms is increasingly critical. A recent focus has been on developing fair versions of classical algorithms, such as those for bandit learning, regression, and clustering. In this work we extend this line of work to include algorithms for optimization subject to one or multiple matroid constraints. We map out a series of results, showing optimal solutions, approximation algorithms, and hardness proofs depending on the specific flavor of the problem. Our algorithms are efficient and empirical experiments demonstrate that fairness can be achieved with a modest compromise to the overall objective value.
BibTeX
@InProceedings{pmlr-v89-chierichetti19a,
title = {Matroids, Matchings, and Fairness},
author = {Chierichetti, Flavio and Kumar, Ravi and Lattanzi, Silvio and Vassilvtiskii, Sergei},
booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
pages = {2212--2220},
year = {2019},
editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
volume = {89},
series = {Proceedings of Machine Learning Research},
month = {16--18 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v89/chierichetti19a/chierichetti19a.pdf},
url = {https://proceedings.mlr.press/v89/chierichetti19a.html},
abstract = {The need for fairness in machine learning algorithms is increasingly critical. A recent focus has been on developing fair versions of classical algorithms, such as those for bandit learning, regression, and clustering. In this work we extend this line of work to include algorithms for optimization subject to one or multiple matroid constraints. We map out a series of results, showing optimal solutions, approximation algorithms, and hardness proofs depending on the specific flavor of the problem. Our algorithms are efficient and empirical experiments demonstrate that fairness can be achieved with a modest compromise to the overall objective value.}
}