Poster
in
Workshop: Structured Probabilistic Inference and Generative Modeling
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Kenza Tazi · Jihao Andreas Lin · ST John · Hong Ge · Richard E Turner · Ross Viljoen · Alex Gardner
Keywords: [ real-world data ] [ Gaussian process ] [ framework ]
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 scaling options. The framework is then applied to a case study of glacier elevation change and yields more accurate results at test time.