Learning invariant representations is an important requirement when training
machine learning models that are driven by spurious correlations in the
datasets. These spurious correlations, between input samples and the target
labels, wrongly direct the neural network predictions resulting in poor
performance on certain groups, especially the minority groups. Robust training
against these spurious correlations requires the knowledge of group membership
for every sample. Such a requirement is impractical in situations where the
data labeling efforts for minority or rare groups are significantly laborious
or where the individuals comprising the dataset choose to conceal sensitive
information. On the other hand, the presence of such data collection efforts
results in datasets that contain partially labeled group information. Recent
works have tackled the fully unsupervised scenario where no labels for groups
are available. Thus, we aim to fill the missing gap in the literature by
tackling a more realistic setting that can leverage partially available
sensitive or group information during training. First, we construct a
constraint set and derive a high probability bound for the group assignment to
belong to the set. Second, we propose an algorithm that optimizes for the
worst-off group assignments from the constraint set. Through experiments on
image and tabular datasets, we show improvements in the minority group's
performance while preserving overall aggregate accuracy across groups.

提出一种利用部分敏感信息训练深度神经网络并改善少数群体预测性能的方法。