  We study the problem of learning classifiers that perform well across (known
or unknown) groups of data. After observing that common worst-group-accuracy
datasets suffer from substantial imbalances, we set out to compare
state-of-the-art methods to simple balancing of classes and groups by either
subsampling or reweighting data. Our results show that these data balancing
baselines achieve state-of-the-art-accuracy, while being faster to train and
requiring no additional hyper-parameters. In addition, we highlight that access
to group information is most critical for model selection purposes, and not so
much during training. All in all, our findings beg closer examination of
benchmarks and methods for research in worst-group-accuracy optimization.
