  When fitting Bayesian machine learning models on scarce data, the main
challenge is to obtain suitable prior knowledge and encode it into the model.
Recent advances in meta-learning offer powerful methods for extracting such
prior knowledge from data acquired in related tasks. When it comes to
meta-learning in Gaussian process models, approaches in this setting have
mostly focused on learning the kernel function of the prior, but not on
learning its mean function. In this work, we explore meta-learning the mean
function of a Gaussian process prior. We present analytical and empirical
evidence that mean function learning can be useful in the meta-learning
setting, discuss the risk of overfitting, and draw connections to other
meta-learning approaches, such as model agnostic meta-learning and functional
PCA.
