Beyond Intuition, a Framework for Applying GPs to Real-World Data
Abstract
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 yielding more accurate results at test time.
Cite
Text
Tazi et al. "Beyond Intuition, a Framework for Applying GPs to Real-World Data." ICML 2023 Workshops: SPIGM, 2023.Markdown
[Tazi et al. "Beyond Intuition, a Framework for Applying GPs to Real-World Data." ICML 2023 Workshops: SPIGM, 2023.](https://mlanthology.org/icmlw/2023/tazi2023icmlw-beyond/)BibTeX
@inproceedings{tazi2023icmlw-beyond,
title = {{Beyond Intuition, a Framework for Applying GPs to Real-World Data}},
author = {Tazi, Kenza and Lin, Jihao Andreas and Viljoen, Ross and Gardner, Alex and John, S. T. and Ge, Hong and Turner, Richard E},
booktitle = {ICML 2023 Workshops: SPIGM},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/tazi2023icmlw-beyond/}
}