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[2510.20035] Throwing Vines at the Wall: Structure Learning via Random Search
Summary
The paper presents a novel approach to structure learning in vine copulas using random search algorithms, outperforming traditional methods in empirical tests.
Why It Matters
This research addresses a significant challenge in machine learning related to multivariate dependence modeling. By improving structure selection through random search and providing theoretical guarantees, it enhances the reliability of statistical models, which is crucial for various applications in data science and AI.
Key Takeaways
- Introduces random search algorithms for structure learning in vine copulas.
- Offers theoretical guarantees on selection probabilities.
- Empirical results show consistent improvement over traditional methods.
- Aims to enhance flexibility in multivariate dependence modeling.
- Serves as a foundation for future ensembling techniques.
Statistics > Methodology arXiv:2510.20035 (stat) [Submitted on 22 Oct 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Throwing Vines at the Wall: Structure Learning via Random Search Authors:Thibault Vatter, Thomas Nagler View a PDF of the paper titled Throwing Vines at the Wall: Structure Learning via Random Search, by Thibault Vatter and Thomas Nagler View PDF HTML (experimental) 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 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:2510.20035v2 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2510.20035 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Thomas Nagler [view email] [v1] Wed, 22 Oct 2025 21:26:18 UTC (414 KB) [v2] Thu, 26 Feb 2026 12:12:35 UTC (395 KB) Full-text links: Access Pa...