@inproceedings{6d797637aeac475f90c6925632d64bce,

title = "Heteroscedastic Gaussian process regression",

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.",

author = "Le, {Quoc V.} and Smola, {Alex J.} and St{\'e}phane Canu",

year = "2005",

doi = "10.1145/1102351.1102413",

language = "English",

isbn = "1595931805",

series = "ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning",

pages = "489--496",

editor = "L. Raedt and S. Wrobel",

booktitle = "ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning",

note = "ICML 2005: 22nd International Conference on Machine Learning ; Conference date: 07-08-2005 Through 11-08-2005",

}