

























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 |
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)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。