About

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. In this website, you’ll find guidelines formalising the decisions of experienced GP practitioners, with an emphasis on kernel design and options for computational scalability.

Papers and abstracts

Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, S. T. John, Hong Ge, and Richard E. Turner. (2023). “Beyond Intuition, a Framework for Applying GPs to Real-World Data.” In ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling. Paper

Kenza Tazi, Jihao Andreas Lin, Lisanne Blok, Alex Gardner, S. T. John, Hong Ge, and Richard E. Turner. (2023). “Towards more interpretable and robust geospatial modelling with Gaussian Processes”. Presented at AGU23, 11-15 Dec. Abstract