Scalable Structure Discovery in Regression using Gaussian Processes
Proceedings of the Workshop on Automatic Machine Learning, PMLR 64:31-40, 2016.
Automatic Bayesian Covariance Discovery (ABCD) in Lloyd et. al (2014) provides a framework for automating statistical modelling as well as exploratory data analysis for regression problems. However ABCD does not scale due to its $O(N^3)$ running time. 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 sophisticated structure. We propose a scalable version of ABCD, to encompass big data within the boundaries of automated statistical modelling.