Multi-group Learning for Hierarchical Groups
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909221930401792 |
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| author | Deng, Samuel Hsu, Daniel |
| author_facet | Deng, Samuel Hsu, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-group Learning for Hierarchical Groups Deng, Samuel Hsu, Daniel Machine Learning 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. |
| title | Multi-group Learning for Hierarchical Groups |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.00258 |