{"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/deep-gaussian-processes-for-multi-fidelity","title":"Deep Gaussian Processes for Multi-fidelity Modeling","arxiv_id":"1903.07320","date":"2019-03-18","proceeding":null,"authors":["Kurt Cutajar","Mark Pullin","Andreas Damianou","Neil Lawrence","Javier González"],"abstract":"Multi-fidelity methods are prominently used when cheaply-obtained, but\npossibly biased and noisy, observations must be effectively combined with\nlimited or expensive true data in order to construct reliable models. This\narises in both fundamental machine learning procedures such as Bayesian\noptimization, as well as more practical science and engineering applications.\nIn this paper we develop a novel multi-fidelity model which treats layers of a\ndeep Gaussian process as fidelity levels, and uses a variational inference\nscheme to propagate uncertainty across them. This allows for capturing\nnonlinear correlations between fidelities with lower risk of overfitting than\nexisting methods exploiting compositional structure, which are conversely\nburdened by structural assumptions and constraints. We show that the proposed\napproach makes substantial improvements in quantifying and propagating\nuncertainty in multi-fidelity set-ups, which in turn improves their\neffectiveness in decision making pipelines.","url_abs":"http://arxiv.org/abs/1903.07320v1","url_pdf":"http://arxiv.org/pdf/1903.07320v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-gaussian-processes-for-multi-fidelity","repo_url":"https://github.com/luck1226/multisource_deepGaussianProcess","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.07320","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}