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Throwing Vines at the Wall: Structure Learning via Random...
Thibault Vat · 2026-05-20 · via cs.LG updates on arXiv.org

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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.
Subjects: Methodology (stat.ME); Machine Learning (cs.LG)
MSC classes: 62H05, 68T05, 62G05
ACM classes: G.3; I.2.6
Cite as: arXiv:2510.20035 [stat.ME]
  (or arXiv:2510.20035v3 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.2510.20035

arXiv-issued DOI via DataCite

Submission history

From: Thibault Vatter [view email]
[v1] Wed, 22 Oct 2025 21:26:18 UTC (414 KB)
[v2] Thu, 26 Feb 2026 12:12:35 UTC (395 KB)
[v3] Tue, 19 May 2026 11:02:03 UTC (404 KB)