Throwing Vines at the Wall: Structure Learning via Random Search
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918510697906176 |
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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 |
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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 |