  Traditional machine learning models focus on achieving good performance on
the overall training distribution, but they often underperform on minority
groups. Existing methods can improve the worst-group performance, but they can
have several limitations: (i) they require group annotations, which are often
expensive and sometimes infeasible to obtain, and/or (ii) they are sensitive to
outliers. Most related works fail to solve these two issues simultaneously as
they focus on conflicting perspectives of minority groups and outliers. We
address the problem of learning group annotations in the presence of outliers
by clustering the data in the space of gradients of the model parameters. We
show that data in the gradient space has a simpler structure while preserving
information about minority groups and outliers, making it suitable for standard
clustering methods like DBSCAN. Extensive experiments demonstrate that our
method significantly outperforms state-of-the-art both in terms of group
identification and downstream worst-group performance.
