Algorithmic development demonstrates simultaneous mean and local variance estimation in nonparametric regression, suggesting improved adaptation to non-uniform noise.
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.
No takes yet. Share an insight, caveat, or question.
Le et al. (2005) studied this question.