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.

翻译：多群体不可知性学习是一个正式的学习标准,它涉及人口分组内预测者有条件的风险,该标准涉及最近的实际问题,如分组公平和隐性分层。本文研究多群体学习问题解决方案的结构,并为学习问题提供简单和接近最佳的算法。