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

Autor:	Tazi, Kenza, Lin, Jihao Andreas, Viljoen, Ross, Gardner, Alex, John, ST, Ge, Hong, Turner, Richard E.
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at the ICML Workshop on Structured Probabilistic Inference and Generative Modelling (2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.03093 Zobrazit plný text záznamu View this record from Arxiv