{"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/learning-gaussian-processes-by-minimizing-pac","title":"Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds","arxiv_id":"1810.12263","date":"2018-10-29","proceeding":"NeurIPS 2018 12","authors":["David Reeb","Andreas Doerr","Sebastian Gerwinn","Barbara Rakitsch"],"abstract":"Gaussian Processes (GPs) are a generic modelling tool for supervised\nlearning. While they have been successfully applied on large datasets, their\nuse in safety-critical applications is hindered by the lack of good performance\nguarantees. 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