Heteroscedastic Gaussian process regression

Authors:
Quoc V. Le;Alex J. Smola;Stéphane Canu
Affiliations:
Australian National University, Australia;National ICT Australia, Australia;PSI - FRE CNRS, INSA de Rouen, France
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

Citing 6
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Abstract

This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Process regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori estimation in exponential families. Unlike previous work, we obtain a convex optimization problem which can be solved via Newton's method.