  In document classification for, e.g., legal and biomedical text, we often
deal with hundreds of classes, including very infrequent ones, as well as
temporal concept drift caused by the influence of real world events, e.g.,
policy changes, conflicts, or pandemics. Class imbalance and drift can
sometimes be mitigated by resampling the training data to simulate (or
compensate for) a known target distribution, but what if the target
distribution is determined by unknown future events? Instead of simply
resampling uniformly to hedge our bets, we focus on the underlying optimization
algorithms used to train such document classifiers and evaluate several
group-robust optimization algorithms, initially proposed to mitigate
group-level disparities. Reframing group-robust algorithms as adaptation
algorithms under concept drift, we find that Invariant Risk Minimization and
Spectral Decoupling outperform sampling-based approaches to class imbalance and
concept drift, and lead to much better performance on minority classes. The
effect is more pronounced the larger the label set.
