Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range densities, a capability that is under-explored by current neural density estimation literature. In these applications, fast and accurate range density estimates over high-dimensional data directly impact user-perceived performance. In this paper, we explore a technique, variable skipping, for accelerating range density estimation over deep autoregressive models. This technique exploits the sparse structure of range density queries to avoid sampling unnecessary variables during approximate inference. We show that variable skipping provides 10-100$\times$ efficiency improvements when targeting challenging high-quantile error metrics, enables complex applications such as text pattern matching, and can be realized via a simple data augmentation procedure without changing the usual maximum likelihood objective.

本文提出一种名为 Variable Skipping 的技术，用于加速基于深度自回归模型的范围密度估计，该技术利用范围密度查询的稀疏结构，在近似推理期间避免采样不必要的变量。通过这种技术，可以在不改变通常的最大似然目标的情况下实现复杂应用，如文本模式匹配，并提供10-100倍的效率提升。同时，我们还展示了数据增强的简单策略来实现 Variable Skipping。

自回归范围密度估计的变量跳过