PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Heteroscedastic Gaussian Process Regression
Miguel Lazaro-Gredilla and Michalis Titsias
In: International Conference on Machine Learning, 28 Jun - 02 Jul, Bellevue, USA.

Abstract

Standard Gaussian processes (GPs) model observations' noise as constant throughout input space. This is often a too restrictive assumption, but one that is needed for GP inference to be tractable. In this work we present a non-standard variational approximation that allows accurate inference in heteroscedastic GPs (i.e., under input-dependent noise conditions). Computational cost is roughly twice that of the standard GP, and also scales as O(n^3). Accuracy is verified by comparing with the golden standard MCMC and its effectiveness is illustrated on several synthetic and real datasets of diverse characteristics. An application to volatility forecasting is also considered.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9089
Deposited By:Miguel Lazaro-Gredilla
Deposited On:21 February 2012