用随机搜索提升藤蔓模型结构学习，效果优于现有方法。
Throwing Vines at the Wall: Structure Learning via Random Search
- 采用随机搜索结合置信集框架优化结构选择
- 在真实数据集上表现持续优于当前最佳方法
- 提供理论保证，适合需要可靠建模的场景
藤蔓耦合模型（vine copulas）在多变量依赖建模中具有灵活性，已广泛应用于机器学习。然而，结构学习仍是关键挑战。早期启发式方法如Dissmann的贪心算法虽被视为黄金标准，但常非最优。本文提出基于随机搜索与模型置信集的统计框架，改进结构选择，提供选择概率与超额风险的理论保障，并可作为集成学习基础。在真实数据集上的实验表明，该方法始终优于现有先进方法。
原文摘要 · Abstract (English)
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.
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