Research map / Learning theory and algorithms / Clustering
Variational Fair Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use variational Fair K-median instead of Backurs et al. 2019 | Fair K-median · Synthetic | K-median objective: 299.859 -> 292.4 | Helped |
| Use variational Fair K-median instead of Backurs et al. 2019 | Fair K-median · Synthetic-unequal | fairness error: 0.77 -> 0.00 | Helped |
| Use variational Fair K-median instead of Backurs et al. 2019 | Fair K-median · Adult | K-median objective: 19330.93 -> 18467.75 | Helped |
| Use variational Fair K-median instead of Backurs et al. 2019 | Fair K-median · Census II | K-median objective: 2385997.92 -> 1754109.46 | Helped |
| Use variational Fair K-means instead of Bera et al. 2019 | Fair K-means · Synthetic | K-means objective: 758.43 -> 207.80 | Helped |
| Use variational Fair K-means instead of Bera et al. 2019 | Fair K-means · Synthetic-unequal | K-means objective: 180.00 -> 159.75 | Helped |
| Use variational Fair K-means instead of Bera et al. 2019 | Fair K-means · Adult | K-means objective: 10913.84 -> 9984.01 | Helped |
| Use variational Fair K-means instead of Bera et al. 2019 | Fair K-means · Bank | fairness error: 0.03 -> 0.05 | Hurt |
| Use variational Fair Ncut instead of Kleindessner et al. 2019 | Fair Ncut · Synthetic-unequal | Ncut objective: 0.03 -> 0.06 | Hurt |
| Use variational Fair Ncut instead of Kleindessner et al. 2019 | Fair Ncut · Adult | Ncut objective: 0.47 -> 0.74 | Hurt |
| Increase the fairness weight λ | Variational fair clustering · Fair-clustering benchmarks | fairness error | Helped |
| Increase the fairness weight λ | Variational fair clustering · Fair-clustering benchmarks | clustering objective | Hurt |
Closest papers by meaning
- Accelerating Spectral Clustering under Fairness Constraints (ICML 2025 · similarity 0.92)
- Fair Clustering via Alignment (ICML 2025 · similarity 0.91)
- The Fairness-Quality Tradeoff in Clustering (NeurIPS 2024 · similarity 0.89)
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.89)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.89)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.88)
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