{"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/practical-bayesian-optimization-for-model","title":"Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search","arxiv_id":"1705.04405","date":"2017-05-11","proceeding":"NeurIPS 2017 12","authors":["Luigi Acerbi","Wei Ji Ma"],"abstract":"Computational models in fields such as computational neuroscience are often\nevaluated via stochastic simulation or numerical approximation. Fitting these\nmodels implies a difficult optimization problem over complex, possibly noisy\nparameter landscapes. Bayesian optimization (BO) has been successfully applied\nto solving expensive black-box problems in engineering and machine learning.\nHere we explore whether BO can be applied as a general tool for model fitting.\nFirst, we present a novel hybrid BO algorithm, Bayesian adaptive direct search\n(BADS), that achieves competitive performance with an affordable computational\noverhead for the running time of typical models. We then perform an extensive\nbenchmark of BADS vs. many common and state-of-the-art nonconvex,\nderivative-free optimizers, on a set of model-fitting problems with real data\nand models from six studies in behavioral, cognitive, and computational\nneuroscience. With default settings, BADS consistently finds comparable or\nbetter solutions than other methods, including `vanilla' BO, showing great\npromise for advanced BO techniques, and BADS in particular, as a general\nmodel-fitting tool.","url_abs":"http://arxiv.org/abs/1705.04405v2","url_pdf":"http://arxiv.org/pdf/1705.04405v2.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":"practical-bayesian-optimization-for-model","repo_url":"https://github.com/Nikhil-Mukund/RIFF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"practical-bayesian-optimization-for-model","repo_url":"https://github.com/acerbilab/bads","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"practical-bayesian-optimization-for-model","repo_url":"https://github.com/acerbilab/pybads","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"practical-bayesian-optimization-for-model","repo_url":"https://github.com/lacerbi/optimviz","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.04405","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}