Simple and near-optimal algorithms for hidden stratification and multi-group learning
Christopher Tosh · Daniel Hsu
Abstract
Multi-group agnostic learning is a formal learning criterion that is concernedwith the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness andhidden stratification. This paper studies the structure of solutions to the multi-group learning problem, and provides simple and near-optimal algorithms for the learningproblem.
Video
Chat is not available.
Successful Page Load