{"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/batch-bayesian-optimization-via-local","title":"Batch Bayesian Optimization via Local Penalization","arxiv_id":"1505.08052","date":"2015-05-29","proceeding":null,"authors":["Javier González","Zhenwen Dai","Philipp Hennig","Neil D. Lawrence"],"abstract":"The popularity of Bayesian optimization methods for efficient exploration of\nparameter spaces has lead to a series of papers applying Gaussian processes as\nsurrogates in the optimization of functions. However, most proposed approaches\nonly allow the exploration of the parameter space to occur sequentially. Often,\nit is desirable to simultaneously propose batches of parameter values to\nexplore. This is particularly the case when large parallel processing\nfacilities are available. These facilities could be computational or physical\nfacets of the process being optimized. E.g. in biological experiments many\nexperimental set ups allow several samples to be simultaneously processed.\nBatch methods, however, require modeling of the interaction between the\nevaluations in the batch, which can be expensive in complex scenarios. We\ninvestigate a simple heuristic based on an estimate of the Lipschitz constant\nthat captures the most important aspect of this interaction (i.e. local\nrepulsion) at negligible computational overhead. The resulting algorithm\ncompares well, in running time, with much more elaborate alternatives. The\napproach assumes that the function of interest, $f$, is a Lipschitz continuous\nfunction. A wrap-loop around the acquisition function is used to collect\nbatches of points of certain size minimizing the non-parallelizable\ncomputational effort. The speed-up of our method with respect to previous\napproaches is significant in a set of computationally expensive experiments.","url_abs":"http://arxiv.org/abs/1505.08052v4","url_pdf":"http://arxiv.org/pdf/1505.08052v4.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":"batch-bayesian-optimization-via-local","repo_url":"https://github.com/SheffieldML/GPyOpt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"efficient-exploration","task_name":"Efficient Exploration"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1505.08052","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}