Modern machine learning models may be susceptible to learning spurious correlations that hold on average but not for the atypical group of samples. To address the problem, previous approaches minimize the empirical worst-group risk. Despite the promise, they often assume that each sample belongs to one and only one group, which does not allow expressing the uncertainty in group labeling. In this paper, we propose a novel framework PG-DRO, which explores the idea of probabilistic group membership for distributionally robust optimization. Key to our framework, we consider soft group membership instead of hard group annotations. The group probabilities can be flexibly generated using either supervised learning or zero-shot approaches. Our framework accommodates samples with group membership ambiguity, offering stronger flexibility and generality than the prior art. We comprehensively evaluate PG-DRO on both image classification and natural language processing benchmarks, establishing superior performance

翻译：现代机器学习模型可能容易学习到在平均情况下成立但对非典型样本群体不成立的虚假相关性。针对这一问题，以往的方法旨在最小化经验最差群体风险。尽管这些方法具有前景，但它们通常假设每个样本仅属于且仅属于一个群体，这一假设无法表达群体标签中的不确定性。在本文中，我们提出了一种新颖的框架PG-DRO，该框架探索了概率群体成员关系在分布鲁棒优化中的应用。我们框架的关键在于采用软群体成员关系而非硬群体标注。群体概率可以通过监督学习或零样本方法灵活生成。与现有技术相比，我们的框架能够处理具有群体成员歧义的样本，提供了更强的灵活性和通用性。我们在图像分类和自然语言处理基准上对PG-DRO进行了全面评估，验证了其优越性能。