{"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/gaussian-process-bandit-optimisation-with","title":"Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations","arxiv_id":null,"date":"2016-12-01","proceeding":"NeurIPS 2016 12","authors":["Kirthevasan Kandasamy","Gautam Dasarathy","Junier B. Oliva","Jeff Schneider","Barnabas Poczos"],"abstract":"In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function $\\func$. Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations to $\\func$ may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of $\\func$ in a small but promising region and speedily identify the optimum. We formalise this task as a \\emph{multi-fidelity} bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop \\mfgpucb, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour, and achieves better regret than strategies which ignore multi-fidelity information. \\mfgpucbs outperforms such naive strategies and other multi-fidelity methods  on several synthetic and real experiments.","url_abs":"http://papers.nips.cc/paper/6118-gaussian-process-bandit-optimisation-with-multi-fidelity-evaluations","url_pdf":"http://papers.nips.cc/paper/6118-gaussian-process-bandit-optimisation-with-multi-fidelity-evaluations.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":"gaussian-process-bandit-optimisation-with","repo_url":"https://github.com/kirthevasank/mf-gp-ucb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}