{"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-selection-for-parallelisation-of","title":"Batch Selection for Parallelisation of Bayesian Quadrature","arxiv_id":"1812.01553","date":"2018-12-04","proceeding":null,"authors":["Ed Wagstaff","Saad Hamid","Michael Osborne"],"abstract":"Integration over non-negative integrands is a central problem in machine\nlearning (e.g. for model averaging, (hyper-)parameter marginalisation, and\ncomputing posterior predictive distributions). Bayesian Quadrature is a\nprobabilistic numerical integration technique that performs promisingly when\ncompared to traditional Markov Chain Monte Carlo methods. However, in contrast\nto easily-parallelised MCMC methods, Bayesian Quadrature methods have, thus\nfar, been essentially serial in nature, selecting a single point to sample at\neach step of the algorithm. We deliver methods to select batches of points at\neach step, based upon those recently presented in the Batch Bayesian\nOptimisation literature. Such parallelisation significantly reduces computation\ntime, especially when the integrand is expensive to sample.","url_abs":"http://arxiv.org/abs/1812.01553v1","url_pdf":"http://arxiv.org/pdf/1812.01553v1.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-selection-for-parallelisation-of","repo_url":"https://github.com/OxfordML/bayesquad","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"},{"task_slug":"numerical-integration","task_name":"Numerical Integration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}