{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:59:31Z","timestamp":1743091171166,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031255984"},{"type":"electronic","value":"9783031255991"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25599-1_13","type":"book-chapter","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T04:32:27Z","timestamp":1678249947000},"page":"167-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6229-5068","authenticated-orcid":false,"given":"Kelly","family":"Smalenberger","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3732-1508","authenticated-orcid":false,"given":"Michael","family":"Smalenberger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Chen, M., Shao, Q., Ibrahim, J.: Monte Carol Methods in Bayesian Computation. Springer, Heidelberg (2000)","DOI":"10.1007\/978-1-4612-1276-8"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Hennig, P., Osborne, M., Girolami, M.: Probabilistic numerics and uncertainty in computations. Proc. Roy. Soc. A: Math. Phys. Eng. Sci. 471(2179) (2015). arXiv:1506.01326","DOI":"10.1098\/rspa.2015.0142"},{"key":"13_CR3","unstructured":"Neal, R.: Probabilistic inference using Markov Chain Monte Carlo methods. Technical Report CRG-TR-93\u20131, University of Toronto (1993)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Blei, D., Kucukelbir, A., McAuliffe, J.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859\u2013877 (2016). arXiv:1601.00670","DOI":"10.1080\/01621459.2017.1285773"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"O'Hagan, A.: Monte Carlo is fundamentally unsound. J. Roy. Stat. Soc. Series D (The Stat.) 36(2), 247\u2013249 (1987)","DOI":"10.2307\/2348519"},{"key":"13_CR6","unstructured":"Wagstaff, E., Hamid, S., Osborne, M.: Batch Selection for Parallelization of Bayesian Quadrature. arXiv: 1812.01553v1 (2018)"},{"key":"13_CR7","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/0378-3758(91)90002-V","volume":"29","author":"A O\u2019Hagan","year":"1991","unstructured":"O\u2019Hagan, A.: Bayes-Hermite quadrature. J. Stat. Plan. Inference 29, 245\u2013260 (1991)","journal-title":"J. Stat. Plan. Inference"},{"issue":"4","key":"13_CR8","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1023\/A:1008832824006","volume":"8","author":"M Kennedy","year":"1998","unstructured":"Kennedy, M.: Bayesian Quadrature with non-normal approximating functions. Stat. Comput. 8(4), 365\u2013375 (1998)","journal-title":"Stat. Comput."},{"key":"13_CR9","unstructured":"Huszar, F., Duvenaud, D.: Optimally-weighted herding in bayesian quadrature. In: From Proceedings of the Twenty-Eight Conference on Uncertainty in Artificial Intelligence. AUAI Press, Arlington (2012)"},{"key":"13_CR10","unstructured":"Osborne, M., et al.: Bayesian quadrature for ratios. In: Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012) (2012)"},{"key":"13_CR11","unstructured":"Gunter, T., Osborne, M., Garnett, R., Hennig, P., Roberts, S.: Sampling for inference in probabilistic models with fast bayesian quadrature. In Advances in neural information processing systems (nips), pp. 1\u20139 (2014). arXiv: 1411.0439v1"},{"key":"13_CR12","unstructured":"Chai, H., Garnett, R.: An Improved Bayesian Framework for Quadrature. arXiv:1802.04782 (2018)"},{"key":"13_CR13","first-page":"46","volume":"1","author":"M Osborne","year":"2012","unstructured":"Osborne, M., Duvenaud, D., Garnett, R., Rasmussen, C., Roberts, S., Ghahramani, Z.: Active learning of model evidence using bayesian quadrature. Adv. Neural. Inf. Process. Syst. 1, 46\u201354 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"13_CR14","unstructured":"Garnett, R., Krishnamurthy, Y., Xiong, X., Schneider, J., Mann, R.: Bayesian optimal active search and surveying. In: Langford, J., Pineau, J. (eds.) Proceedings of the 29th International Conference on Machine Learning (ICML 2012), Omnipress, Madison, WI, USA (2012)"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Nguyen, V., Rana, S., Gupta, S., Li, C., Venkatesh, S.: Budgeted batch bayesian optimization with unknown batch sizes. In IEEE International Conference on Data Mining, ICDM, pp. 1107\u20131112. arXiv:1703.04842 (2017)","DOI":"10.1109\/ICDM.2016.0144"},{"issue":"2","key":"13_CR16","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1023\/A:1008923215028","volume":"11","author":"R Neal","year":"2001","unstructured":"Neal, R.: Annealed importance Sampling. Stat. Comput. 11(2), 125\u2013139 (2001)","journal-title":"Stat. Comput."},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1063\/1.1835238","volume":"735","author":"J Skilling","year":"2004","unstructured":"Skilling, J.: Nested Sampling. Bayesian Inference Max. Entropy Methods Sci. Eng. 735, 395\u2013405 (2004)","journal-title":"Bayesian Inference Max. Entropy Methods Sci. Eng."},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Diaconis, P.: Bayesian numerical analysis. In: Statistical Decision Theory and Related Topics IV, pp. 163\u2013175. Springer, New York (1988)","DOI":"10.1007\/978-1-4613-8768-8_20"},{"key":"13_CR19","unstructured":"Minka, T.: Deriving quadrature Rules from Gaussian processes. Technical report, Statistics Department, Carnegie Mellon University, pp. 1\u201321 (2000)"},{"key":"13_CR20","unstructured":"Rasmussen, C.E., Ghahramani, Z., Becker, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15. MIT Press, Cambridge (2003)"},{"key":"13_CR21","volume-title":"Gaussian Processes for Machine Learning","author":"CE Rasmussen","year":"2006","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)"},{"key":"13_CR22","unstructured":"Briol, F., Oates, C., Girolami, M., Osborne, M., Sejdinovic, D.: Probabilistic Integration: A Role in Statistical Computation?, pp. 1\u201349. arXiv:1512.00933 (2015)"},{"key":"13_CR23","series-title":"Adaptation Learning and Optimization","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-642-10701-6_6","volume-title":"Computational Intelligence in Expensive Optimization Problems","author":"D Ginsbourger","year":"2010","unstructured":"Ginsbourger, D., Le Riche, R., Carraro, L.: Kriging is well-suited to parallelize optimization. In: Tenne, Y., Goh, C.-K. (eds.) Computational Intelligence in Expensive Optimization Problems. ALO, vol. 2, pp. 131\u2013162. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-10701-6_6"},{"key":"13_CR24","unstructured":"Gonzales, J., Dai, Z., Hennig, P., Lawrence, N.: Batch Bayesian optimization via local penalization. In: Artificial intelligence and statistics, pp. 648\u2013657 (2016)"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Smalenberger, K., Smalenberger, M.: On the cessation criteria for batch bayesian quadrature using future uncertainty sampling. The University of North Carolina at Charlotte (2022)","DOI":"10.1007\/978-3-031-25599-1_13"},{"issue":"9","key":"13_CR26","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1093\/comjnl\/bxq003","volume":"53","author":"R Garnett","year":"2010","unstructured":"Garnett, R., Osborne, M., Reece, S., Rogers, A., Roberts, S.: Sequential Bayesian prediction in the presence of changepoints and faults. Comput. J. 53(9), 1430 (2010)","journal-title":"Comput. J."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25599-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T17:10:42Z","timestamp":1680714642000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25599-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031255984","9783031255991"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25599-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}