Summary of Multi-group Learning For Hierarchical Groups, by Samuel Deng and Daniel Hsu
Multi-group Learning for Hierarchical Groups
by Samuel Deng, Daniel Hsu
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The multi-group learning model is a framework for machine learning where a single predictor must perform well on multiple, possibly overlapping subgroups of interest. In this paper, the authors extend their previous work to include hierarchically structured groups. They design an algorithm that produces an interpretable and deterministic decision tree predictor with near-optimal sample complexity. The authors then evaluate their algorithm empirically using real datasets with hierarchical group structure and find that it achieves attractive generalization properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to teach machines to learn about different groups of things, like people or objects. It’s called multi-group learning, and it helps the machine make good predictions about each group even if they’re related to each other in some way. The authors created an algorithm that can do this and tested it on real data to see how well it works. |
Keywords
* Artificial intelligence * Decision tree * Generalization * Machine learning
