标题： 向墙投掷藤蔓：通过随机搜索进行结构学习 显示英文标题

标题： Throwing Vines at the Wall: Structure Learning via Random Search

摘要： 藤 copulas 提供了灵活的多变量依赖建模，并已在机器学习中得到广泛应用，但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，仍然被认为是黄金标准，但通常次优。 我们提出了改进结构选择的随机搜索算法以及基于模型置信集的统计框架，该框架提供了选择概率的理论保证，并为集成提供了强大的基础。 在多个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。 显示英文摘要

摘要： Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning, yet structure learning remains a key challenge. Early heuristics like the greedy algorithm of Dissmann are still considered the gold standard, but often suboptimal. We propose random search algorithms that improve structure selection and a statistical framework based on model confidence sets, which provides theoretical guarantees on selection probabilities and a powerful foundation for ensembling. Empirical results on several real-world data sets show that our methods consistently outperform state-of-the-art approaches.