{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beyond-intuition-a-framework-for-applying-gps","title":"Beyond Intuition, a Framework for Applying GPs to Real-World Data","arxiv_id":"2307.03093","date":"2023-07-06","proceeding":null,"authors":["Kenza Tazi","Jihao Andreas Lin","Ross Viljoen","Alex Gardner","ST John","Hong Ge","Richard E. Turner"],"abstract":"Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. 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