Multi-group Learning for Hierarchical Groups
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要