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.

翻译：藤蔓Copula提供了灵活的多元依赖建模方法，已在机器学习中得到广泛应用。然而，结构学习仍然是一个关键挑战。早期的启发式方法（如Dissmann的贪心算法）仍被视为黄金标准，但往往并非最优。我们提出了随机搜索算法和基于模型置信集的统计框架，以改进结构选择、提供选择概率的理论保证，并为集成学习奠定基础。在真实数据集上的实证结果表明，我们的方法始终优于最先进的方法。