Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

Automating statistical modelling is a challenging problem that has far-reaching implications for artificial intelligence. The Automatic Statistician employs a kernel search algorithm to provide a first step in this direction for regression problems. However this does not scale due to its $O(N^3)$ running time for the model selection. This is undesirable not only because the average size of data sets is growing fast, but also because there is potentially more information in bigger data, implying a greater need for more expressive models that can discover finer structure. We propose Scalable Kernel Composition (SKC), a scalable kernel search algorithm, to encompass big data within the boundaries of automated statistical modelling.