论文标题
彗星流：朝着多元极端和尾部依赖性的生成建模
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence
论文作者
论文摘要
正常化的流量是一种流行的深层生成模型，通常无法代表在现实世界过程中观察到的极端现象。特别是，现有的归一化流量体系结构难以模拟多元极端，其特征是变量之间的重尾边缘分布和不对称的尾巴依赖性。鉴于这一缺点，我们提出了彗星（Copula多元极端）流，该彗星将联合分布建模为两个部分的过程：（i）对其边际分布进行建模，以及（ii）对其副群体分布进行建模。彗星流通过将边缘极端分位数的参数尾信仰与中质量的经验核密度函数相结合，从而捕获了重尾的边缘分布。此外，彗星流通过查看诱导特征空间中低维的歧管结构等依赖性来捕获多元极端之间的不对称尾巴依赖性。与其他最先进的基线架构相比，合成和现实世界数据集的实验结果都证明了彗星流在捕获重尾边缘和不对称尾部依赖性方面的有效性。所有代码均可在https://github.com/andrewmcdonald27/cometflows上在github上找到。
Normalizing flows, a popular class of deep generative models, often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-tailed marginal distributions and asymmetric tail dependence among variables. In light of this shortcoming, we propose COMET (COpula Multivariate ExTreme) Flows, which decompose the process of modeling a joint distribution into two parts: (i) modeling its marginal distributions, and (ii) modeling its copula distribution. COMET Flows capture heavy-tailed marginal distributions by combining a parametric tail belief at extreme quantiles of the marginals with an empirical kernel density function at mid-quantiles. In addition, COMET Flows capture asymmetric tail dependence among multivariate extremes by viewing such dependence as inducing a low-dimensional manifold structure in feature space. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of COMET Flows in capturing both heavy-tailed marginals and asymmetric tail dependence compared to other state-of-the-art baseline architectures. All code is available on GitHub at https://github.com/andrewmcdonald27/COMETFlows.
