We consider the problem of training a classification model with group
annotated training data. Recent work has established that, if there is
distribution shift across different groups, models trained using the standard
empirical risk minimization (ERM) objective suffer from poor performance on
minority groups and that group distributionally robust optimization (Group-DRO)
objective is a better alternative. The starting point of this paper is the
observation that though Group-DRO performs better than ERM on minority groups
for some benchmark datasets, there are several other datasets where it performs
much worse than ERM. Inspired by ideas from the closely related problem of
domain generalization, this paper proposes a new and simple algorithm that
explicitly encourages learning of features that are shared across various
groups. The key insight behind our proposed algorithm is that while Group-DRO
focuses on groups with worst regularized loss, focusing instead, on groups that
enable better performance even on other groups, could lead to learning of
shared/common features, thereby enhancing minority performance beyond what is
achieved by Group-DRO. Empirically, we show that our proposed algorithm matches
or achieves better performance compared to strong contemporary baselines
including ERM and Group-DRO on standard benchmarks on both minority groups and
across all groups. Theoretically, we show that the proposed algorithm is a
descent method and finds first order stationary points of smooth nonconvex
functions.

本论文探讨的是使用组注释的数据进行分类模型训练的问题，提出了一个新的算法，旨在通过鼓励学习跨各种组共享的特征来提高少数族裔的性能。通过在标准基准测试中进行实证研究，证明了本文提出的算法在不同组别和少数族裔上的表现要好于 ERM 和 Group-DRO 等强基线。同时，论文也从理论上证明了所提出算法是一种下降方法，并且可以找到平稳点。