Multi-group Learning for Hierarchical Groups
Samuel Deng Daniel Hsu
The multi-group learning model formalizes the learning scenario in which a
single predictor must generalize well on multiple, possibly overlapping
subgroups of interest. We extend the study of multi-group learning to the
natural case where the groups are hierarchically structured. We design an
algorithm for this setting that outputs an interpretable and deterministic
decision tree predictor with near-optimal sample complexity. We then conduct an
empirical evaluation of our algorithm and find that it achieves attractive
generalization properties on real datasets with hierarchical group structure.