@InProceedings{plagemann08ecml,
  title     = {Nonstationary Gaussian Process Regression using Point Estimates
    of Local Smoothness},
  author    = {Plagemann, C. and Kersting, K. and Burgard, W.},
  booktitle = {Proc.~of the European Conference on Machine Learning (ECML)},
  address   = {Antwerp, Belgium},
  year      = {2008},
  abstract  = {
Gaussian processes using nonstationary covariance functions are a
powerful tool for Bayesian regression with input-dependent
smoothness. A common approach is to model the local smoothness by a
latent process that is integrated over using Markov chain Monte Carlo
approaches. In this paper, we demonstrate that an approximation that
uses the estimated mean of the local smoothness yields good results
and allows one to employ efficient gradient-based optimization
techniques for jointly learning the parameters of the latent and the
observed processes. Extensive experiments on both synthetic and
real-world data, including challenging problems in robotics, show the
relevance and feasibility of our approach.
},
  note      = {To appear},
  pdfurl    = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann08ecml.pdf}
}