Research map / Learning theory and algorithms / Clustering
Better Algorithms for Individually Fair $k$-Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LP rounding tailored to cost contributions and fairness radii | Fair-(p,k)-Clustering · UCI bank, census, and diabetes | maximum fairness violation: 1.27 | Helped |
| Use LP rounding tailored to cost contributions and fairness radii | Fair-k-Means · UCI bank, census, and diabetes | cost relative to LP cost: \%1; \%15 | Helped |
| Use Fair-Round instead of the compared fair-clustering methods | Fair-k-Means · UCI bank, census, and diabetes | maximum fairness violation: 1.3 | Mixed |
| Relax the LP fairness radii to match Mahabadi and Vakilian's violation level | Fair-Round · UCI k-Means experiments | clustering cost | Helped |
| Sparsify the instance before solving the LP (ablation) | Fair-Round · UCI bank, census, and diabetes | runtime | Helped |
| Sparsify the instance before solving the LP (ablation) | Fair-Round · UCI bank, census, and diabetes | clustering cost | No clear effect |
| Sparsify the instance before solving the LP (ablation) | Fair-Round · UCI bank, census, and diabetes | maximum fairness violation | No clear effect |
| Set the sparsification parameter δ to the reported dataset-specific values (ablation) | Sparse-Fair-Round · UCI bank, census, and diabetes | runtime: δ = 0.3, 0.05 and 0.01 | Helped |
Closest papers by meaning
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.91)
- Fair Clustering via Alignment (ICML 2025 · similarity 0.90)
- The Fairness-Quality Tradeoff in Clustering (NeurIPS 2024 · similarity 0.90)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.90)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.89)
- Parameterized Approximation Schemes for Fair-Range Clustering (NeurIPS 2024 · similarity 0.88)
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