arXiv:2611.07693 · v1

Non-Parametric Mutual Information Estimation with Vine Copulas

首次提交 2026-11-05 17:18

版本更新 2026-11-05 17:18

雷达观测 2026-11-06 11:06

摘要

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, which we can estimate non-parametrically. This structural simplification yields VINE, a flexible and computationally efficient MI estimator. We compare VINE against SotA neural MI estimators on a range of datasets, both under settings where the simplified D-vine assumption holds and where it is violated, demonstrating competitive accuracy at a lower computational cost for low- to medium-dimensional datasets.

作者备注：Accepted at the conference on Neural Information Processing Systems (NeurIPS 2026)