Gaussian process quantile regression using expectation propagation


Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagation algorithm. We describe how this work relates to other quantile regression methods and apply the method on both synthetic and real data sets. The method is shown to be competitive with state of the art methods whilst allowing for the leverage of the full Gaussian process probabilistic framework.

Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Event Title: 29th International Conference on Machine Learning
Event Type: Other
Event Dates: 2012-06-26 - 2012-07-01
Uncontrolled Keywords: Gaussian process,probablisitc modelling,decision theory
Full Text Link: https://arxiv.o ... g/abs/1206.6391
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Paper
Published Date: 2012-06-27
Authors: Boukouvalas, Alexios
Barillec, Remi
Cornford, Dan (ORCID Profile 0000-0001-8787-6758)



Version: Accepted Version

License: ["licenses_description_other" not defined]

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