Research map / Learning theory and algorithms / Clustering
Efficient Testing for Correlation Clustering: Improved Algorithms and Optimal Bounds
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
- Fast Combinatorial Algorithms for Min Max Correlation Clustering (ICML 2023 · similarity 0.92)
- Instance-Specific Approximation Ratios for Correlation Clustering and Max-Cut (ICML 2026 · similarity 0.92)
- Towards Better-than-2 Approximation for Constrained Correlation Clustering (ICML 2025 · similarity 0.90)
- Improved Approximation Algorithms for Chromatic and Pseudometric-Weighted Correlation Clustering (NeurIPS 2025 · similarity 0.90)
- Learning-Augmented Streaming Algorithms for Correlation Clustering (NeurIPS 2025 · similarity 0.89)
- Local Graph Clustering with Noisy Labels (ICLR 2024 · similarity 0.89)
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.
