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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 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 t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
19th International Conference on Artificial Intelligence and Statistics Journal website
Host title:
19th International Conference on Artificial Intelligence and Statistics
Publication date:
2016-05-01
Acceptance date:
2016-03-30
Source identifiers:
613114
Pubs id:
pubs:613114
UUID:
uuid:9b3fde87-d9fb-404f-9be3-3a13b2fb4afd
Local pid:
pubs:613114
Deposit date:
2016-04-04

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