Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

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William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric Xing ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:1013-1021, 2016.

Abstract

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to flexibly define a change surface in combination with expressive spectral mixture kernels to capture the complex statistical structure. Finally, through the use of novel methods for additive non-separable kernels, we can scale the model to large datasets. We demonstrate the model on numerical and real world data, including a large spatio-temporal disease dataset where we identify previously unknown heterogeneous changes in space and time.

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