[2403.02187] Mutual Information Estimation via Normalizing Flows
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Computer Science > Machine Learning

arXiv:2403.02187 (cs)
[Submitted on 4 Mar 2024 (v1), last revised 25 May 2024 (this version, v3)]

Title:Mutual Information Estimation via Normalizing Flows

Authors:Ivan Butakov, Alexander Tolmachev, Sofia Malanchuk, Anna Neopryatnaya, Alexey Frolov
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Abstract:We propose a novel approach to the problem of mutual information (MI) estimation via introducing a family of estimators based on normalizing flows. The estimator maps original data to the target distribution, for which MI is easier to estimate. We additionally explore the target distributions with known closed-form expressions for MI. Theoretical guarantees are provided to demonstrate that our approach yields MI estimates for the original data. Experiments with high-dimensional data are conducted to highlight the practical advantages of the proposed method.
Comments: 20 pages, 6 figures
Subjects: Machine Learning (cs.LG); Information Theory (cs.IT); Machine Learning (stat.ML)
Cite as: arXiv:2403.02187 [cs.LG]
  (or arXiv:2403.02187v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2403.02187

Submission history

From: Ivan Butakov [view email]
[v1] Mon, 4 Mar 2024 16:28:04 UTC (123 KB)
[v2] Tue, 5 Mar 2024 12:43:12 UTC (123 KB)
[v3] Sat, 25 May 2024 09:37:21 UTC (73 KB)
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