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arXiv · 2112.12181

Simple and near-optimal algorithms for hidden stratification and multi-group learning

Christopher Tosh·Daniel Hsu
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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.

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Christopher Tosh, Daniel Hsu. 2021-12-22. Simple and near-optimal algorithms for hidden stratification and multi-group learning. https://arxiv.org/abs/2112.12181

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