{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T15:29:28Z","timestamp":1726068568315},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030414030"},{"type":"electronic","value":"9783030414047"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41404-7_32","type":"book-chapter","created":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T12:02:58Z","timestamp":1582372978000},"page":"453-466","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Bayesian Optimization Based on Parallel Sequential Random Embeddings"],"prefix":"10.1007","author":[{"given":"Noriko","family":"Yokoyama","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Kohjima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatsushi","family":"Matsubayashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroyuki","family":"Toda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"key":"32_CR1","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems (NIPS), pp. 2951\u20132959. Curran Associates Inc. (2012)"},{"key":"32_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-03098-8_4","volume-title":"PRIMA 2018: Principles and Practice of Multi-Agent Systems","author":"H Kiyotake","year":"2018","unstructured":"Kiyotake, H., Kohjima, M., Matsubayashi, T., Toda, H.: Multi agent flow estimation based on Bayesian optimization with time delay and low dimensional parameter conversion. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds.) PRIMA 2018. LNCS (LNAI), vol. 11224, pp. 53\u201369. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-030-03098-8_4"},{"key":"32_CR3","unstructured":"Brochu, E., Cora, V.M., de Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning (2010). \nhttp:\/\/arXiv.org\/abs\/1012.2599"},{"issue":"4","key":"32_CR4","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"D Jones","year":"1998","unstructured":"Jones, D., Schonlau, M., Welch, W.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455\u2013492 (1998)","journal-title":"J. Global Optim."},{"key":"32_CR5","volume-title":"Gaussian Process for Machine Learning","author":"CE Rasmussen","year":"2006","unstructured":"Rasmussen, C.E., Williams, C.K.: Gaussian Process for Machine Learning. MIT Press, Cambridge (2006)"},{"key":"32_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1613\/jair.4806","volume":"55","author":"Z Wang","year":"2016","unstructured":"Wang, Z., Hutter, F., Zoghi, M., Matheson, D., de Freitas, N.: Bayesian optimization in a billion dimensions via random embeddings. J. Artif. Intell. Res. (JAIR) 55, 361\u2013387 (2016)","journal-title":"J. Artif. Intell. Res. (JAIR)"},{"key":"32_CR7","unstructured":"Kandasamy, K., Krishnamurthy, A., Schneider, J., Poczos, B.: Parallelised Bayesian optimisation via Thompson sampling. In: Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 84, pp. 133\u2013142. PMLR (2018)"},{"key":"32_CR8","unstructured":"Kandasamy, K., Schneider, J., Poczos, B.: High dimensional Bayesian optimisation and bandits via additive models. In: Proceedings of the 32nd International Conference on Machine Learning (ICML), vol. 37, pp. 295\u2013304 (2015)"},{"key":"32_CR9","unstructured":"Rolland, P., Scarlett, J., Bogunovic, I., Cevher, V.: High dimensional Bayesian optimization via additive models with overlapping groups. In: International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 298\u2013307 (2018)"},{"key":"32_CR10","unstructured":"Qian, H., Hu, Y., Yu, Y.: Derivative-free optimization of high-dimensional non-convex functions by sequential random embeddings. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), pp. 1946\u20131952. AAAI Press, New York (2016)"},{"issue":"Oct","key":"32_CR11","first-page":"2879","volume":"12","author":"AD Bull","year":"2011","unstructured":"Bull, A.D.: Convergence rates of efficient global optimization algorithms. J. Mach. Learn. Res. 12(Oct), 2879\u20132904 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"32_CR12","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1016\/j.jspi.2010.04.018","volume":"140","author":"E Vazquez","year":"2010","unstructured":"Vazquez, E., Bect, J.: Convergence properties of the expected improvement algorithm with fixed mean and covariance functions. J. Stat. Plann. Infer. 140(11), 3088\u20133095 (2010)","journal-title":"J. Stat. Plann. Infer."},{"key":"32_CR13","volume-title":"Bayesian Approach to Global Optimization: Theory and Applications","author":"J Mockus","year":"2012","unstructured":"Mockus, J.: Bayesian Approach to Global Optimization: Theory and Applications, vol. 37. Springer, Dordrecht (2012)"},{"key":"32_CR14","volume-title":"Handbook of Genetic Algorithms","author":"L Davis","year":"1991","unstructured":"Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)"},{"issue":"1","key":"32_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/106365603321828970","volume":"11","author":"N Hansen","year":"2003","unstructured":"Hansen, N., Muller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1\u201318 (2003)","journal-title":"Evol. Comput."},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Hansen, N., Auger, A., Ros, R., Finck, S., Posik, P.: Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO), pp. 1689\u20131696. ACM, New York (2010)","DOI":"10.1145\/1830761.1830790"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41404-7_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T12:09:35Z","timestamp":1582373375000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41404-7_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030414030","9783030414047"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41404-7_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2019.org\/","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":"214","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":"125","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":"58% - 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":"3","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":"2","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)"}},{"value":"for ACPR 2019 Workshops volume accepted 17 full papers and 6 short papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}