Beyond Intuition, a Framework for Applying GPs to Real-World Data
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 6 Jul 2023.
Builds on
21 of 43 references.
References · 21
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- 2017Streaming Sparse Gaussian Process Approximationscited by 41
9 more
- 2014Automatic Construction and Natural-Language Description of Nonparametric Regression Modelscited by 40
- 2022Additive Gaussian Processes Revisitedcited by 19
- 2021Kernel Identification Through Transformerscited by 13
- 2018BRUNO: A Deep Recurrent Model for Exchangeable Datacited by 11
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- 2022Kernel Learning for Explainable Climate Sciencecited by 3
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Citing papers per year: 2024: 1.
1 paper.