Can Gaussian Process Regression Be Made Robust Against Model Mismatch?

Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), exhibiting e.g. multiple overfitting maxima and logarithmically slow learning. I investigate whether GPs can be made robust against such effects by adapting student model hyperparameters to maximize the evidence (data likelihood). An approximation for the average evidence is derived and used to predict the optimal hyperparameter values and the resulting generalization error. For large input space dimension, where the approximation becomes exact, Bayes-optimal performance is obtained at the evidence maximum, but the actual hyperparameters (e.g. the noise level) do not necessarily reflect the properties of the teacher. Also, the theoretically achievable evidence maximum cannot always be reached with the chosen set of hyperparameters, and maximizing the evidence in such cases can actually make generalization p...

• Type: Conference
• Year: 2004
• Where: DSMML
• Authors: Peter Sollich
• Keywords: DSMML 2004, Evidence Maximum, Generalization Error, Machine Learning, Slow Learning