Summary of Learning with Multi-group Guarantees For Clusterable Subpopulations, by Jessica Dai et al.
Learning With Multi-Group Guarantees For Clusterable Subpopulations
by Jessica Dai, Nika Haghtalab, Eric Zhao
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 proposed work tackles the challenge of ensuring performance guarantees not only for the overall population but also for meaningful subpopulations. The key idea is to define subpopulations based on naturally emerging clusters from the data distribution, viewed as a mixture model with relevant components. Two formalisms are introduced: attributing each individual to the most likely component or all components proportional to their likelihood. A multi-objective algorithm is designed to provide guarantees for both approaches while handling various subpopulation structures simultaneously. The study uses online calibration as a case study, achieving an O(T^{1/2}) rate even when subpopulations are not well-separated. This outperforms the cluster-then-predict approach, which requires separation between median subgroup features and achieves a slower O(T^{2/3}) rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers aim to make predictions better for specific groups within a population. They suggest two ways to define these groups: grouping people together based on their similarities or giving each person a mix of group memberships. A special algorithm is designed to provide good guarantees for both approaches while handling different types of group structures. The study uses an example from online learning, achieving a faster rate than previous methods that require the groups to be well-separated. |
Keywords
» Artificial intelligence » Likelihood » Mixture model » Online learning
