Multi-group Learning for Hierarchical Groups

Samuel Deng; Daniel Hsu

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arXiv · 2402.00258

Multi-group Learning for Hierarchical Groups

Samuel Deng·Daniel Hsu

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Abstract

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.

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Samuel Deng, Daniel Hsu. 2024-02-01. Multi-group Learning for Hierarchical Groups. https://arxiv.org/abs/2402.00258

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