Gaussian Processes (GPs) offer an attractive method for regression over
small, structured and correlated datasets. However, their deployment is
hindered by computational costs and limited guidelines on how to apply GPs
beyond simple low-dimensional datasets. We propose a framework to identify the
suitability of GPs to a given problem and how to set up a robust and
well-specified GP model. The guidelines formalise the decisions of experienced
GP practitioners, with an emphasis on kernel design and options for
computational scalability. The framework is then applied to a case study of
glacier elevation change yielding more accurate results at test time.Comment: Accepted at the 1st ICML Workshop on Structured Probabilistic
Inference and Generative Modelling (2023