Beyond Intuition, a Framework for Applying GPs to Real-World Data

Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, ST John +2 more
7/6/2023
cs.LGstat.ML

Official code: kenzaxtazi/icml23-gpframe (5 stars, last commit 2023-07-16).

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 options for computational scalability. The framework is then applied to a case study of glacier elevation change yielding more accurate results at test time.

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Code Implementations(1)

kenzaxtazi/icml23-gpframeOfficial

Code and data for 'Beyond Intuition, a Framework for Applying GPs to Real-World Data' presented at the Structured Probabilistic Inference and Generative Modeling Workshop at ICML 2023.

50Python, Jupyter NotebookJun 27, 20233 years ago

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Cite this paper

@article{tazi2023beyond,
  title  = {Beyond Intuition, a Framework for Applying GPs to Real-World Data},
  author = {Kenza Tazi and Jihao Andreas Lin and Ross Viljoen and Alex Gardner and ST John and Hong Ge and Richard E. Turner},
  year   = {2023},
  eprint = {2307.03093},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.03093v2}
}

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