{"ID":2851938,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20035","arxiv_id":"2510.20035","title":"Throwing Vines at the Wall: Structure Learning via Random Search","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.20035","url_pdf":"https://arxiv.org/pdf/2510.20035v3","authors":"[\"Thibault Vatter\",\"Thomas Nagler\"]","published":"2025-10-22T21:26:18Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"cs.LG\"]","methods":"[]","has_code":false}