Robust Regression with Twinned Gaussian Processes

We propose a Gaussian process (GP) framework for robust inference in which a GP prior on the mixing weights of a two-component noise model augments the standard process over latent function values. This approach is a generalization of the mixture likelihood used in traditional robust GP regression, and a specialization of the GP mixture models suggested by Tresp [1] and Rasmussen and Ghahramani [2]. The value of this restriction is in its tractable expectation propagation updates, which allow for faster inference and model selection, and better convergence than the standard mixture. An additional benefit over the latter method lies in our ability to incorporate knowledge of the noise domain to influence predictions, and to recover with the predictive distribution information about the outlier distribution via the gating process. The model has asymptotic complexity equal to that of conventional robust methods, but yields more confident predictions on benchmark problems than classica...

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Andrew Naish-Guzman, Sean B. Holden
• Keywords: GP Mixture Models, Information Technology, NIPS 2007, Noise Models, Robust Gp Regression