{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T15:10:07Z","timestamp":1761664207359,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030676605"},{"type":"electronic","value":"9783030676612"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-67661-2_41","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T07:06:46Z","timestamp":1614150406000},"page":"691-706","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bayesian Optimization with Missing Inputs"],"prefix":"10.1007","author":[{"given":"Phuc","family":"Luong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santu","family":"Rana","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":[[2021,2,25]]},"reference":[{"issue":"3","key":"41_CR1","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1177\/0962280206074466","volume":"16","author":"G Ambler","year":"2007","unstructured":"Ambler, G., Omar, R.Z., Royston, P.: A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome. Stat. Methods Med. Res. 16(3), 277\u2013298 (2007)","journal-title":"Stat. Methods Med. Res."},{"issue":"3","key":"41_CR2","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/s12911-016-0318-z","volume":"16","author":"L Beretta","year":"2016","unstructured":"Beretta, L., Santaniello, A.: Nearest neighbor imputation algorithms: a critical evaluation. BMC Med. Inform. Decis. Making 16(3), 74 (2016). https:\/\/doi.org\/10.1186\/s12911-016-0318-z","journal-title":"BMC Med. Inform. Decis. Making"},{"issue":"1","key":"41_CR3","first-page":"7133","volume":"18","author":"D Bertsimas","year":"2017","unstructured":"Bertsimas, D., Pawlowski, C., Zhuo, Y.D.: From predictive methods to missing data imputation: an optimization approach. J. Mach. Learn. Res. 18(1), 7133\u20137171 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"41_CR4","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. arXiv preprint arXiv:1012.2599 (2010)"},{"issue":"10","key":"41_CR5","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1016\/j.jclinepi.2006.01.014","volume":"59","author":"ART Donders","year":"2006","unstructured":"Donders, A.R.T., Van Der Heijden, G.J., Stijnen, T., Moons, K.G.: A gentle introduction to imputation of missing values. Clin. Epidemiol. 59(10), 1087\u20131091 (2006)","journal-title":"Clin. Epidemiol."},{"key":"41_CR6","series-title":"Springer Series in Materials Science","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-23871-5_3","volume-title":"Information Science for Materials Discovery and Design","author":"PI Frazier","year":"2016","unstructured":"Frazier, P.I., Wang, J.: Bayesian optimization for materials design. In: Lookman, T., Alexander, F.J., Rajan, K. (eds.) Information Science for Materials Discovery and Design. SSMS, vol. 225, pp. 45\u201375. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-23871-5_3"},{"key":"41_CR7","unstructured":"Gardner, J., Pleiss, G., Weinberger, K.Q., Bindel, D., Wilson, A.G.: GPytorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration. In: NIPS. pp. 7576\u20137586 (2018)"},{"key":"41_CR8","unstructured":"Gupta, S., Shilton, A., Rana, S., Venkatesh, S.: Exploiting strategy-space diversity for batch Bayesian optimization. In: Artificial Intelligence and Statistics (AISTATS), pp. 538\u2013547 (2018)"},{"key":"41_CR9","unstructured":"Hern\u00e1ndez-Lobato, J.M., Hoffman, M.W., Ghahramani, Z.: Predictive entropy search for efficient global optimization of black-box functions. In: Advances in Neural Information Processing Systems, pp. 918\u2013926 (2014)"},{"issue":"4","key":"41_CR10","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). https:\/\/doi.org\/10.1023\/A:1008306431147","journal-title":"J. Global Optim."},{"issue":"5","key":"41_CR11","doi-asserted-by":"publisher","first-page":"402","DOI":"10.4097\/kjae.2013.64.5.402","volume":"64","author":"H Kang","year":"2013","unstructured":"Kang, H.: The prevention and handling of the missing data. Korean J. Anesthesiol 64(5), 402\u2013406 (2013)","journal-title":"Korean J. Anesthesiol"},{"issue":"1","key":"41_CR12","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1115\/1.3653121","volume":"86","author":"HJ Kushner","year":"1964","unstructured":"Kushner, H.J.: A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Basic Eng. J. 86(1), 97\u2013106 (1964)","journal-title":"Basic Eng. J."},{"issue":"117\u2013129","key":"41_CR13","first-page":"2","volume":"2","author":"J Mockus","year":"1978","unstructured":"Mockus, J., Tiesis, V., Zilinskas, A.: The application of Bayesian methods for seeking the extremum. Towards Glob. Optim. 2(117\u2013129), 2 (1978)","journal-title":"Towards Glob. Optim."},{"key":"41_CR14","unstructured":"Neal, R.M.: Probabilistic inference using Markov chain Monte Carlo methods. Department of Computer Science, University of Toronto Toronto, ON, Canada (1993)"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Gupta, S., Rana, S., Shilton, A., Venkatesh, S.: Bayesian optimization for categorical and category-specific continuous inputs. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i04.5971"},{"key":"41_CR16","unstructured":"Oliveira, R., Ott, L., Ramos, F.: Bayesian optimisation under uncertain inputs. arXiv preprint arXiv:1902.07908 (2019)"},{"key":"41_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-540-28650-9_4","volume-title":"Advanced Lectures on Machine Learning","author":"CE Rasmussen","year":"2004","unstructured":"Rasmussen, C.E.: Gaussian processes in machine learning. In: Bousquet, O., von Luxburg, U., R\u00e4tsch, G. (eds.) ML -2003. LNCS (LNAI), vol. 3176, pp. 63\u201371. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-28650-9_4"},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Rohmer, E., Singh, S.P., Freese, M.: V-rep: A versatile and scalable robot simulation framework. In: Intelligent Robots and Systems (IROS), pp. 1321\u20131326. IEEE (2013)","DOI":"10.1109\/IROS.2013.6696520"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: ICML, pp. 880\u2013887 (2008)","DOI":"10.1145\/1390156.1390267"},{"issue":"1","key":"41_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2016","unstructured":"Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., De Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148\u2013175 (2016)","journal-title":"Proc. IEEE"},{"key":"41_CR21","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.knosys.2019.02.034","volume":"173","author":"M \u015amieja","year":"2019","unstructured":"\u015amieja, M., Struski, \u0141., Tabor, J., Marzec, M.: Generalized RBF Kernel for incomplete data. Knowl.-Based Syst. 173, 150\u2013162 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"41_CR22","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: NIPS, pp. 2951\u20132959 (2012)"},{"issue":"5","key":"41_CR23","doi-asserted-by":"publisher","first-page":"3250","DOI":"10.1109\/TIT.2011.2182033","volume":"58","author":"N Srinivas","year":"2012","unstructured":"Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.W.: Information-theoretic regret bounds for Gaussian process optimization in the bandit setting. IEEE Trans. Inf. Theory 58(5), 3250\u20133265 (2012)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"6","key":"41_CR24","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11348","volume":"10","author":"F Tang","year":"2017","unstructured":"Tang, F., Ishwaran, H.: Random forest missing data algorithms. Stat. Anal. Data Min. ASA Data Sci. J. 10(6), 363\u2013377 (2017)","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"41_CR25","first-page":"213","volume":"5","author":"R Wagner","year":"1991","unstructured":"Wagner, R., Kampmann, R., Voorhees, P.W.: Homogeneous second phase precipitation. Phase Transform. Mater. 5, 213\u2013303 (1991)","journal-title":"Phase Transform. Mater."},{"key":"41_CR26","unstructured":"Yoon, J., Jordon, J., Van Der Schaar, M.: Gain: Missing data imputation using generative adversarial nets. arXiv preprint arXiv:1806.02920 (2018)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67661-2_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T23:03:42Z","timestamp":1740351822000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67661-2_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030676605","9783030676612"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67661-2_41","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":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd2020.net\/","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":"945","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":"195","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":"21% - 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":"4,5","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":"4,4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","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)"}}]}}