Fair Hierarchical Clustering
Abstract
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering.
Cite
Text
Ahmadian et al. "Fair Hierarchical Clustering." Neural Information Processing Systems, 2020.Markdown
[Ahmadian et al. "Fair Hierarchical Clustering." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/ahmadian2020neurips-fair/)BibTeX
@inproceedings{ahmadian2020neurips-fair,
title = {{Fair Hierarchical Clustering}},
author = {Ahmadian, Sara and Epasto, Alessandro and Knittel, Marina and Kumar, Ravi and Mahdian, Mohammad and Moseley, Benjamin and Pham, Philip and Vassilvitskii, Sergei and Wang, Yuyan},
booktitle = {Neural Information Processing Systems},
year = {2020},
url = {https://mlanthology.org/neurips/2020/ahmadian2020neurips-fair/}
}