Throwing Vines at the Wall: Structure Learning via Random Search

Thibault Vatter; Thomas Nagler

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arXiv · 2510.20035

Throwing Vines at the Wall: Structure Learning via Random Search

Thibault Vatter·Thomas Nagler

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Abstract

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.

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Thibault Vatter, Thomas Nagler. 2025-10-22. Throwing Vines at the Wall: Structure Learning via Random Search. https://arxiv.org/abs/2510.20035

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