Throwing Vines at the Wall: Structure Learning via Random Search

Fuente: arXiv
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Main Authors: Vatter, Thibault, Nagler, Thomas
Format: Preprint
Published: 2025
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author Vatter, Thibault
Nagler, Thomas
author_facet Vatter, Thibault
Nagler, Thomas
contents Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning. Yet, structure learning remains a key challenge. Early heuristics, such as Dissmann's greedy algorithm, are still considered the gold standard but are often suboptimal. We propose random search algorithms and a statistical framework based on model confidence sets, to improve structure selection, provide theoretical guarantees on selection probabilities and excess risk, as well as serve as a foundation for ensembling. Empirical results on real-world data sets show that our methods consistently outperform state-of-the-art approaches.
format Preprint
id arxiv_https___arxiv_org_abs_2510_20035
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Throwing Vines at the Wall: Structure Learning via Random Search
Vatter, Thibault
Nagler, Thomas
Methodology
Machine Learning
62H05, 68T05, 62G05
G.3; I.2.6
Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning. Yet, structure learning remains a key challenge. Early heuristics, such as Dissmann's greedy algorithm, are still considered the gold standard but are often suboptimal. We propose random search algorithms and a statistical framework based on model confidence sets, to improve structure selection, provide theoretical guarantees on selection probabilities and excess risk, as well as serve as a foundation for ensembling. Empirical results on real-world data sets show that our methods consistently outperform state-of-the-art approaches.
title Throwing Vines at the Wall: Structure Learning via Random Search
topic Methodology
Machine Learning
62H05, 68T05, 62G05
G.3; I.2.6
url https://arxiv.org/abs/2510.20035