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Heteroscedastic gaussian process regression
quoc le, Alex Smola and Stéphane Canu
In: ICML 2005, July 2005, Bonn.
AbstractThis 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 vari-
ance local ly unlike standard Gaussian Pro-
cess 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 op-
timization problem which can be solved via
Newton’s method. [Edit]
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