  In real-world classification tasks, each class often comprises multiple
finer-grained "subclasses." As the subclass labels are frequently unavailable,
models trained using only the coarser-grained class labels often exhibit highly
variable performance across different subclasses. This phenomenon, known as
hidden stratification, has important consequences for models deployed in
safety-critical applications such as medicine. We propose GEORGE, a method to
both measure and mitigate hidden stratification even when subclass labels are
unknown. We first observe that unlabeled subclasses are often separable in the
feature space of deep neural networks, and exploit this fact to estimate
subclass labels for the training data via clustering techniques. We then use
these approximate subclass labels as a form of noisy supervision in a
distributionally robust optimization objective. We theoretically characterize
the performance of GEORGE in terms of the worst-case generalization error
across any subclass. We empirically validate GEORGE on a mix of real-world and
benchmark image classification datasets, and show that our approach boosts
worst-case subclass accuracy by up to 22 percentage points compared to standard
training techniques, without requiring any prior information about the
subclasses.
