Heteroscedastic Gaussian Process Regression
quoc le, Alex Smola and Stéphane Canu
In: ICML 2005, 08 Aug -12 Aug, Germany.

Abstract

This paper presents an algorithm to estimate simultaneously both mean and variance of a {\scanu non parametric} regression problem. The key point is that we are able to estimate variance \emph{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.

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