Variable Skipping for Autoregressive Range Density Estimation
This addresses the need for fast and accurate range density estimates over high-dimensional data, which directly impacts user-perceived performance in domains like databases, though it is incremental as it builds on existing autoregressive models.
The paper tackles the problem of accelerating range density estimation in deep autoregressive models for applications like database cardinality estimation, achieving 10-100× efficiency improvements in high-quantile error metrics and enabling complex tasks such as text pattern matching.
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.