- William Herlands, A. Wilson, H. Nickisch, S. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, E. Xing. 2015. Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. 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.
