CMX Lunch Seminar

Gaussian Process models offer elegant possibilities to encode invariances and equivariances. Choosing adapted mean and covariance functions enables going around data augmentation or making it somehow implicit. Furthermore, resulting models do not only propagate invariances and equivariances via the predictive mean but also via posterior simulations. We review related results and illustrations pertaining to invariances, and tackle recent work pertaining to equivariances, introducing in turn via examples some recent research about computationally efficient equivariant GP modelling. Based on several collaborations to be further detailed during the presentation.