Research map / Learning theory and algorithms / Clustering
Linear Time Algorithms for Individually Fair k-means via Multi-Swap Local Search
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
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 0.90)
- Parameterized Approximation Schemes for Fair-Range Clustering (NeurIPS 2024 · similarity 0.89)
- Towards a Theoretical Understanding of Why Local Search Works for Clustering with Fair-Center Representation (AAAI 2024 · similarity 0.89)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.89)
- Fair Clustering via Alignment (ICML 2025 · similarity 0.88)
- Relative Error Fair Clustering in the Weak-Strong Oracle Model (ICML 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.