{"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/phoenics-a-universal-deep-bayesian-optimizer","title":"PHOENICS: A universal deep Bayesian optimizer","arxiv_id":"1801.01469","date":"2018-01-04","proceeding":null,"authors":["Florian Häse","Loïc M. Roch","Christoph Kreisbeck","Alán Aspuru-Guzik"],"abstract":"In this work we introduce PHOENICS, a probabilistic global optimization\nalgorithm combining ideas from Bayesian optimization with concepts from\nBayesian kernel density estimation. We propose an inexpensive acquisition\nfunction balancing the explorative and exploitative behavior of the algorithm.\nThis acquisition function enables intuitive sampling strategies for an\nefficient parallel search of global minima. The performance of PHOENICS is\nassessed via an exhaustive benchmark study on a set of 15 discrete,\nquasi-discrete and continuous multidimensional functions. Unlike optimization\nmethods based on Gaussian processes (GP) and random forests (RF), we show that\nPHOENICS is less sensitive to the nature of the co-domain, and outperforms GP\nand RF optimizations. We illustrate the performance of PHOENICS on the\nOregonator, a difficult case-study describing a complex chemical reaction\nnetwork. We demonstrate that only PHOENICS was able to reproduce qualitatively\nand quantitatively the target dynamic behavior of this nonlinear reaction\ndynamics. We recommend PHOENICS for rapid optimization of scalar, possibly\nnon-convex, black-box unknown objective functions.","url_abs":"http://arxiv.org/abs/1801.01469v1","url_pdf":"http://arxiv.org/pdf/1801.01469v1.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":"phoenics-a-universal-deep-bayesian-optimizer","repo_url":"https://github.com/aspuru-guzik-group/phoenics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"global-optimization","task_name":"global-optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}