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New VINE estimator offers efficient mutual information calculation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed VINE, a novel non-parametric estimator for mutual information that utilizes simplified D-vine copulas. This approach simplifies the density ratio into a product of pair copulas, enabling efficient and flexible estimation. VINE demonstrates competitive accuracy compared to state-of-the-art neural MI estimators, particularly for low- to medium-dimensional datasets, while requiring less computational resources. AI

IMPACT This research could lead to more efficient methods for analyzing complex datasets in machine learning.

RANK_REASON The cluster contains an academic paper detailing a new method for mutual information estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New VINE estimator offers efficient mutual information calculation

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The cluster contains an academic paper detailing a new method for mutual information estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sigurd Holmsen, Berit Omli {\O}ksnes, Ingrid Hob{\ae}k Haff, Sylvia Richardson, Ali Ramezani-Kebrya ·

    Non-Parametric Mutual Information Estimation with Vine Copulas

    arXiv:2611.07693v1 Announce Type: new Abstract: We introduce VINE: a non-parametric mutual information (MI) estimator based on simplified D-vine copulas. Approximating the joint density with a simplified D-vine allows the density ratio to simplify to a product of pair copulas, wh…

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