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arXiv AI By Alberto Foresti, Ivan Butakov, Alexander Tolmachev, Giulio Franzese, Alexey Frolov, Pietro Michiardi

Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation

Read the original on arXiv AI →

arXiv:2607. 03487v1 Announce Type: cross Abstract: Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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