Research map / Learning theory and algorithms / Clustering
Approximation Algorithms for Fair Range Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a structured LP with half-integral solutions and flow-based rounding | Fair range ℓ_p-clustering · Fair range ℓ_p-clustering | approximation factor: constant factor | Helped |
| Extend the algorithm to zero-opening-cost facilities | Proposed fair range clustering algorithm · Fair range facility location with zero opening costs | approximation factor: e^{O(p)} | Helped |
Closest papers by meaning
- Capacitated Fair-Range Clustering: Hardness and Approximation Algorithms (ICML 2026 · similarity 0.93)
- Parameterized Approximation Schemes for Fair-Range Clustering (NeurIPS 2024 · similarity 0.92)
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- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.89)
- Accelerating Spectral Clustering under Fairness Constraints (ICML 2025 · similarity 0.88)
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