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Information-Theoretic Active Correlation Clustering (2402.03587v2)

Published 5 Feb 2024 in cs.LG and stat.ML

Abstract: We study correlation clustering where the pairwise similarities are not known in advance. For this purpose, we employ active learning to query pairwise similarities in a cost-efficient way. We propose a number of effective information-theoretic acquisition functions based on entropy and information gain. We extensively investigate the performance of our methods in different settings and demonstrate their superior performance compared to the alternatives.

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