Cold-Start Active Correlation Clustering
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866910044490039296 |
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| author | Aronsson, Linus Wu, Han Chehreghani, Morteza Haghir |
| author_facet | Aronsson, Linus Wu, Han Chehreghani, Morteza Haghir |
| contents | We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where no true initial pairwise similarities are available for active learning. To address this challenge, we propose a coverage-aware method that encourages diversity early in the process. We demonstrate the effectiveness of our approach through several synthetic and real-world experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cold-Start Active Correlation Clustering Aronsson, Linus Wu, Han Chehreghani, Morteza Haghir Machine Learning Artificial Intelligence Social and Information Networks We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where no true initial pairwise similarities are available for active learning. To address this challenge, we propose a coverage-aware method that encourages diversity early in the process. We demonstrate the effectiveness of our approach through several synthetic and real-world experiments. |
| title | Cold-Start Active Correlation Clustering |
| topic | Machine Learning Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2509.25376 |