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

本文提出了一种新的概率组成员身份的框架 PG-DRO，以解决现代机器学习模型易受特定样本组内平均相关性的干扰的问题，通过考虑软组成员身份而不是硬组注释，该框架可以灵活生成组概率，从而更好地适应样本组成员身份模糊的情况，并在图像分类和自然语言处理基准测试中得到了优越性能。