Standard learning approaches are designed to perform well on average for the data distribution available at training time. Developing learning approaches that are not overly sensitive to the training distribution is central to research on domain- or out-of-distribution generalization, robust optimization and fairness. In this work we focus on links between research on domain generalization and algorithmic fairness -- where performance under a distinct but related test distributions is studied -- and show how the two fields can be mutually beneficial. While domain generalization methods typically rely on knowledge of disjoint "domains" or "environments", "sensitive" label information indicating which demographic groups are at risk of discrimination is often used in the fairness literature. Drawing inspiration from recent fairness approaches that improve worst-case performance without knowledge of sensitive groups, we propose a novel domain generalization method that handles the more realistic scenario where environment partitions are not provided. We then show theoretically and empirically how different partitioning schemes can lead to increased or decreased generalization performance, enabling us to outperform Invariant Risk Minimization with handcrafted environments in multiple cases. We also show how a re-interpretation of IRMv1 allows us for the first time to directly optimize a common fairness criterion, group-sufficiency, and thereby improve performance on a fair prediction task.
翻译:设计标准学习方法是为了在培训时间提供的数据分配方面实现良好的平均效果; 开发对培训分配不过分敏感的学习方法,是研究领域或范围外的普及、稳健优化和公平性的核心; 在这项工作中,我们注重领域一般化和算法公平研究之间的联系 -- -- 在对不同但相关的测试分布下的业绩进行研究的情况下,研究这两个领域的绩效 -- -- 并显示这两个领域如何可以相互受益; 域一般化方法通常依赖脱节的“域”或“环境”、“敏感”标签知识; 说明哪些人口群体面临歧视风险的标签信息经常用于公平文献中; 借鉴近期的公平方法,在没有敏感群体知识的情况下改善最坏情况的业绩,我们提出一种新的一般化方法,处理环境分布不提供更现实的情景; 然后,我们从理论上和从经验上展示不同分割计划如何导致总体性业绩的提高或降低,从而使我们能够在多种情况下超越手工制作的环境的不变风险最小化。 我们还表明,如何重新解释IRMv1,使我们第一次能够对业绩进行最公平的预测,从而直接优化共同标准。