ICML 2022spotlight22 citations
Simple and near-optimal algorithms for hidden stratification and multi-group learning
Christopher J Tosh, Daniel Hsu
Abstract
Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness and hidden stratification. This paper studies the structure of solutions to the multi-group learning problem, and provides simple and near-optimal algorithms for the learning problem.
BibTeX
@InProceedings{pmlr-v162-tosh22a,
title = {Simple and near-optimal algorithms for hidden stratification and multi-group learning},
author = {Tosh, Christopher J and Hsu, Daniel},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {21633--21657},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/tosh22a/tosh22a.pdf},
url = {https://proceedings.mlr.press/v162/tosh22a.html},
abstract = {Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness and hidden stratification. This paper studies the structure of solutions to the multi-group learning problem, and provides simple and near-optimal algorithms for the learning problem.}
}