Accuracy estimation of neural networks by extreme value theory
Machine Learning
2025-11-04 v1 Machine Learning
Probability
Abstract
Neural networks are able to approximate any continuous function on a compact set. However, it is not obvious how to quantify the error of the neural network, i.e., the remaining bias between the function and the neural network. Here, we propose the application of extreme value theory to quantify large values of the error, which are typically relevant in applications. The distribution of the error beyond some threshold is approximately generalized Pareto distributed. We provide a new estimator of the shape parameter of the Pareto distribution suitable to describe the error of neural networks. Numerical experiments are provided.
Keywords
Cite
@article{arxiv.2511.00490,
title = {Accuracy estimation of neural networks by extreme value theory},
author = {Gero Junike and Marco Oesting},
journal= {arXiv preprint arXiv:2511.00490},
year = {2025}
}