Scalable Gaussian processes for characterizing multidimensional change surfaces

William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, Eric Xing

Research output: Contribution to conferencePaper

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.

Original languageEnglish (US)
Pages1013-1021
Number of pages9
StatePublished - Jan 1 2016
Event19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 - Cadiz, Spain
Duration: May 9 2016May 11 2016

Conference

Conference19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016
CountrySpain
CityCadiz
Period5/9/165/11/16

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability

Cite this

Herlands, W., Wilson, A., Nickisch, H., Flaxman, S., Neill, D., van Panhuis, W., & Xing, E. (2016). Scalable Gaussian processes for characterizing multidimensional change surfaces. 1013-1021. Paper presented at 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain.