{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:28:32Z","timestamp":1743150512121,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757649"},{"type":"electronic","value":"9783030757656"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-75765-6_21","type":"book-chapter","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T09:08:54Z","timestamp":1620378534000},"page":"257-268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sparse Spectrum Gaussian Process for Bayesian Optimization"],"prefix":"10.1007","author":[{"given":"Ang","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santu","family":"Rana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"issue":"4","key":"21_CR1","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455\u2013492 (1998)","journal-title":"J. Global Optim."},{"issue":"1","key":"21_CR2","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2015","unstructured":"Shahriari, B., et al.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148\u2013175 (2015)","journal-title":"Proc. IEEE"},{"key":"21_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/978-3-319-97310-4_29","volume-title":"PRICAI 2018: Trends in Artificial Intelligence","author":"A Yang","year":"2018","unstructured":"Yang, A., Li, C., Rana, S., Gupta, S., Venkatesh, S.: Efficient Bayesian optimisation using derivative meta-model. In: Geng, X., Kang, B.-H. (eds.) PRICAI 2018. LNCS (LNAI), vol. 11013, pp. 256\u2013264. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-97310-4_29"},{"key":"21_CR4","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: NeurIPS, pp. 2951\u20132959 (2012)"},{"key":"21_CR5","doi-asserted-by":"publisher","first-page":"5683","DOI":"10.1038\/s41598-017-05723-0","volume":"7","author":"C Li","year":"2017","unstructured":"Li, C., et al.: Rapid Bayesian optimisation for synthesis of short polymer fiber materials. Sci. Rep. 7, 5683 (2017)","journal-title":"Sci. Rep."},{"issue":"10","key":"21_CR6","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Long, M., et al.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision (2013)","DOI":"10.1109\/ICCV.2013.274"},{"key":"21_CR8","unstructured":"Jasper, S., et al.: Scalable Bayesian using deep neural networks. In: ICML (2015)"},{"key":"21_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-642-25566-3_40","volume-title":"Learning and Intelligent Optimization","author":"F Hutter","year":"2011","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507\u2013523. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25566-3_40"},{"key":"21_CR10","unstructured":"Snelson, E., et al.: Sparse GP using pseudo-inputs. In: NeurIPS (2006)"},{"key":"21_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-030-03991-2_46","volume-title":"AI 2018: Advances in Artificial Intelligence","author":"A Yang","year":"2018","unstructured":"Yang, A., Li, C., Rana, S., Gupta, S., Venkatesh, S.: Sparse approximation for Gaussian process with derivative observations. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 507\u2013518. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03991-2_46"},{"key":"21_CR12","unstructured":"Titsias, M.: Variational learning of inducing variables in SGP. In: AISTATS (2009)"},{"key":"21_CR13","first-page":"1865","volume":"11","author":"G Lazaro","year":"2010","unstructured":"Lazaro, G., et al.: Sparse spectrum gaussian process regression. J. Mach. Learn. Res. 11, 1865\u20131881 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"151","key":"21_CR14","first-page":"1","volume":"18","author":"J Hensman","year":"2017","unstructured":"Hensman, J., Durrande, N., Solin, A., et al.: Variational Fourier features for Gaussian processes. J. Mach. Learn. Res. 18(151), 1\u2013151 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR15","unstructured":"Wang, K., et al.: Exact GP on a million data points. In: NeurIPS (2019)"},{"key":"21_CR16","first-page":"1809","volume":"13","author":"P Hennig","year":"2012","unstructured":"Hennig, P., Schuler, C.J.: Entropy search for information-efficient global optimization. J. Mach. Learn. Res. 13, 1809\u20131837 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR17","unstructured":"Hern\u00e1ndez, J.M., Hoffman, M.W., Ghahramani, Z.: Predictive entropy search for efficient global optimization of black-box functions. In: NeurIPS, pp. 918\u2013926 (2014)"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., et al.: Gaussian Processes for Machine Learning, vol.\u00a01 (2006)","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"21_CR19","unstructured":"Srinivas, N., et al.: Gaussian process optimization in the bandit setting: no regret and experimental design. arXiv preprint arXiv:0912.3995 (2009)"},{"key":"21_CR20","doi-asserted-by":"publisher","DOI":"10.1515\/9781400881994","volume-title":"Lectures on Fourier Integrals","author":"S Bochner","year":"1959","unstructured":"Bochner, S.: Lectures on Fourier Integrals. Princeton University Press, Princeton (1959)"},{"key":"21_CR21","unstructured":"Bijl, H., et al.: A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization. arXiv preprint arXiv:1604.00169 (2016)"},{"issue":"1","key":"21_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10618600.1996.10474692","volume":"5","author":"G Kitagawa","year":"1996","unstructured":"Kitagawa, G.: Monte Carlo filter and smoother for non-gaussian nonlinear state space models. J. Comput. Graph. Stat. 5(1), 1\u201325 (1996)","journal-title":"J. Comput. Graph. Stat."},{"key":"21_CR23","unstructured":"Finkel, D.E.: DIRECT Optimization Algorithm User Guide. CRSC (2003)"},{"issue":"2","key":"21_CR24","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0364-5916(02)00037-8","volume":"26","author":"JO Andersson","year":"2002","unstructured":"Andersson, J.O., Helander, T., H\u00f6glund, L., Shi, P., Sundman, B.: Thermo-Calc & DICTRA, computational tools for materials science. Calphad 26(2), 273\u2013312 (2002)","journal-title":"Calphad"},{"key":"21_CR25","unstructured":"Saunders, N., et\u00a0al.: CALPHAD: A Comprehensive Guide. Elsevier (1998)"},{"key":"21_CR26","unstructured":"Dheeru, D., Karra Taniskidou, E.: UCI Machine Learning Repository (2017)"},{"key":"21_CR27","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.eswa.2018.08.023","volume":"115","author":"TT Joy","year":"2019","unstructured":"Joy, T.T., et al.: A flexible transfer learning framework for Bayesian optimization with convergence guarantee. Exp. Syst. Appl. 115, 656\u2013672 (2019)","journal-title":"Exp. Syst. Appl."}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75765-6_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T09:51:05Z","timestamp":1725011465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75765-6_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757649","9783030757656"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75765-6_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"8 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"23% - 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":"7","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)"}}]}}