Heteroscedastic Gaussian process regression

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.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Alexander J. Smola, Quoc V. Le, Stéphane Canu
• Keywords: Convex Optimization Problem, Gaussian Process Regression, ICML 2005, Machine Learning, Parametric Regression Problem