# Towards a pretrained deep learning estimator of the Linfoot informational correlation

arXiv: https://arxiv.org/abs/2512.12358 · HTML: https://mlsanity.com/paper/2512.12358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 13 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Beyond Normal: On the Evaluation of Mutual Information Estimators](https://mlsanity.com/paper/2306.11078/) (2023)
- [On the Properties and Estimation of Pointwise Mutual Information Profiles](https://mlsanity.com/paper/2310.10240/) (2023)

## Abstract (arXiv)

We develop a supervised deep-learning approach to estimate mutual information between two continuous random variables. As labels, we use the Linfoot informational correlation, a transformation of mutual information that has many important properties. Our method is based on ground truth labels for Gaussian and Clayton copulas. We compare our method with estimators based on kernel density, k-nearest neighbours and neural estimators. We show generally lower bias and lower variance. As a proof of principle, future research could look into training the model with a more diverse set of examples from other copulas for which ground truth labels are available.