Scalable gaussian processes for characterizing multidimensional change surfaces - Herlands, W; Wilson, A; Nickisch, H; Flaxman, S; Neill, D; van Panhuis, W; Xing, E (Journal of Machine Learning Research, 2016)
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.