Record link: 
http://hdl.handle.net/20.500.12708/230091
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Title: 
Instance-Specific Approximation Ratios for Correlation Clustering and Max-Cut
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Citation: 
Lüderssen, S. J., Bercea, I.-O., & Neumann, S. (2026). Instance-Specific Approximation Ratios for Correlation Clustering and Max-Cut. In Forty-third International Conference on Machine Learning : ICML 2026. Forty-Third International Conference on Machine Learning (ICML 2026), Seoul, Korea (the Republic of).
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Lüderssen, Sebastian Johannes 
Bercea, Ioana-Oriana 
Neumann, Stefan 
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Organisational Unit: 
E194-06 - Forschungsbereich Machine Learning 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
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Published in: 
Forty-third International Conference on Machine Learning : ICML 2026 
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Date (published): 
2026
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Event name: 
Forty-Third International Conference on Machine Learning (ICML 2026) 
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Event date: 
6-Jul-2026 - 11-Jul-2026
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Event place: 
Seoul, Korea (the Republic of)
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Number of Pages: 
19
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Peer reviewed: 
Yes
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Keywords: 
algorithms; correlation clustering; max-cut
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Project title: 
Towards Trustworthy Recommendation Systems for Online Social Networks: VRG23-013 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds) 
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Research Areas: 
Mathematical and Algorithmic Foundations: 100%
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Science Branch: 
1020 - Informatik: 90%
5020 - Wirtschaftswissenschaften: 10%
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Appears in Collections:
Conference Paper

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