Summary of Proportional Fairness in Non-centroid Clustering, by Ioannis Caragiannis et al.
Proportional Fairness in Non-Centroid Clustering
by Ioannis Caragiannis, Evi Micha, Nisarg Shah
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
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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 paper revisits the proportionally fair clustering framework, which aims to provide stronger group fairness guarantees for large and cohesive groups of data points. The existing framework is extended to non-centroid clustering by adapting two proportional fairness criteria: core and fully justified representation (FJR). This adaptation enables the use of different loss functions that consider the relationships between agents in a cluster. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper updates an old way of grouping things together so they’re fair for everyone. Right now, it only works well if there’s a big group with lots of similar things inside. The new version makes it work better by taking into account how the different things relate to each other. |
Keywords
» Artificial intelligence » Clustering
