Scalable gaussian processes for characterizing multidimensional change surfaces

Conference item

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 exibly 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.

Authors: Herlands, W; Wilson, A; Nickisch, H; Flaxman, S; Neill, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Host title: 19th International Conference on Artificial Intelligence and Statistics
• Journal: 19th International Conference on Artificial Intelligence and Statistics
• Publication date: 2016-05-01
• Acceptance date: 2016-03-30