  Recent work has shown that standard training via empirical risk minimization
(ERM) can produce models that achieve high accuracy on average but low accuracy
on underrepresented groups due to the prevalence of spurious features. A
predominant approach to tackle this group robustness problem minimizes the
worst group error (akin to a minimax strategy) on the training data, hoping it
will generalize well on the testing data. However, this is often suboptimal,
especially when the out-of-distribution (OOD) test data contains previously
unseen groups. Inspired by ideas from the information retrieval and
learning-to-rank literature, this paper first proposes to use Discounted
Cumulative Gain (DCG) as a metric of model quality for facilitating better
hyperparameter tuning and model selection. Being a ranking-based metric, DCG
weights multiple poorly-performing groups (instead of considering just the
group with the worst performance). As a natural next step, we build on our
results to propose a ranking-based training method called Discounted Rank
Upweighting (DRU), which differentially reweights a ranked list of
poorly-performing groups in the training data to learn models that exhibit
strong OOD performance on the test data. Results on several synthetic and
real-world datasets highlight the superior generalization ability of our
group-ranking-based (akin to soft-minimax) approach in selecting and learning
models that are robust to group distributional shifts.
