Learning Invariances using the Marginal Likelihood

Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the predictions to be invariant to transformations of the input that are known to be irrelevant (e.g.