{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T19:40:40Z","timestamp":1726083640965},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030474355"},{"type":"electronic","value":"9783030474362"}],"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\/978-3-030-47436-2_62","type":"book-chapter","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T07:02:47Z","timestamp":1588921367000},"page":"827-839","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Level Set Estimation with Search Space Warping"],"prefix":"10.1007","author":[{"given":"Manisha","family":"Senadeera","sequence":"first","affiliation":[]},{"given":"Santu","family":"Rana","sequence":"additional","affiliation":[]},{"given":"Sunil","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,6]]},"reference":[{"issue":"3","key":"62_CR1","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s11222-011-9241-4","volume":"22","author":"J Bect","year":"2012","unstructured":"Bect, J., Ginsbourger, D., Li, L., Picheny, V., V\u00e1zquez, E.: Sequential design of computer experiments for the estimation of a probability of failure. Stat. Comput. 22(3), 773\u2013793 (2012). \nhttps:\/\/doi.org\/10.1007\/s11222-011-9241-4","journal-title":"Stat. Comput."},{"key":"62_CR2","unstructured":"Bogunovic, I., Scarlett, J., Krause, A., Cevher, V.: Truncated variance reduction: a unified approach to Bayesian optimization and level-set estimation. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 1515\u20131523. Curran Associates Inc., USA (2016)"},{"key":"62_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. CoRR abs\/1012.2599 (2010). \nhttp:\/\/arxiv.org\/abs\/1012.2599"},{"key":"62_CR4","unstructured":"Bryan, B., Nichol, R.C., Genovese, C.R., Schneider, J., Miller, C.J., Wasserman, L.: Active learning for identifying function threshold boundaries. In: Weiss, Y., Sch\u00f6lkopf, B., Platt, J.C. (eds.) Advances in Neural Information Processing Systems, vol. 18, pp. 163\u2013170. MIT Press (2006)"},{"issue":"4","key":"62_CR5","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1080\/00401706.2013.860918","volume":"56","author":"C Chevalier","year":"2014","unstructured":"Chevalier, C., Bect, J., Ginsbourger, D., Vazquez, E., Picheny, V., Richet, Y.: Fast parallel kriging based stepwise uncertainty reduction with application to the identification of an excursion set. Technometrics 56(4), 455\u2013465 (2014)","journal-title":"Technometrics"},{"key":"62_CR6","unstructured":"Eppinger, R., Kuppa, S., Saul, R., Sun, E.: Supplement: development of improved injury criteria for the assessment of advanced automotive restraint systems: Ii (2000)"},{"key":"62_CR7","doi-asserted-by":"crossref","unstructured":"Garg, A., et al.: Tumor localization using automated palpation with Gaussian process adaptive sampling. In: 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 194\u2013200. IEEE (2016)","DOI":"10.1109\/COASE.2016.7743380"},{"key":"62_CR8","unstructured":"Gotovos, A., Casati, N., Hitz, G., Krause, A.: Active learning for level set estimation. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 1344\u20131350. AAAI Press (2013)"},{"key":"62_CR9","unstructured":"Hallquist, J.O., Manual, L.D.T.: Livermore software technology corporation. Livermore, CA (1998)"},{"key":"62_CR10","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/S0364-5916(02)00037-8","volume":"26","author":"J-O Andersson","year":"2002","unstructured":"Andersson, J.-O., Helander, T., H\u00f6glund, L., Shi, P., Sundman, B.: Thermo-Calc and DICTRA, computational tools for materials science. Calphad 26, 273\u2013312 (2002)","journal-title":"Calphad"},{"key":"62_CR11","unstructured":"Paciorek, C.J., Schervish, M.J.: Nonstationary covariance functions for Gaussian process regression. In: Thrun, S., Saul, L.K., Sch\u00f6lkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, pp. 273\u2013280. MIT Press (2004)"},{"key":"62_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-540-87481-2_14","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"C Plagemann","year":"2008","unstructured":"Plagemann, C., Kersting, K., Burgard, W.: Nonstationary Gaussian process regression using point estimates of local smoothness. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 204\u2013219. Springer, Heidelberg (2008). \nhttps:\/\/doi.org\/10.1007\/978-3-540-87481-2_14"},{"key":"62_CR13","volume-title":"Gaussian Processes for Machine Learning, Adaptive Computation and Machine Learning","author":"C Rasmussen","year":"2006","unstructured":"Rasmussen, C., Williams, C.: Gaussian Processes for Machine Learning, Adaptive Computation and Machine Learning. MIT Press, Cambridge (2006)"},{"key":"62_CR14","unstructured":"Snoek, J., Swersky, K., Zemel, R., Adams, R.P.: Input warping for Bayesian optimization of non-stationary functions. In: Proceedings of the 31st International Conference on International Conference on Machine Learning, ICML 2014, vol. 32, pp. II-1674\u2013II-1682 (2014). \nJMLR.org"},{"key":"62_CR15","unstructured":"Srinivas, N., Krause, A., Kakade, S., Seeger, M.: Gaussian process optimization in the bandit setting: no regret and experimental design. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML 2010, pp. 1015\u20131022. Omnipress, USA (2010)"},{"key":"62_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1007\/978-3-030-10928-8_17","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Zanette","year":"2019","unstructured":"Zanette, A., Zhang, J., Kochenderfer, M.J.: Robust super-level set estimation using Gaussian processes. In: Berlingerio, M., Bonchi, F., G\u00e4rtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11052, pp. 276\u2013291. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-10928-8_17"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-47436-2_62","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T09:11:30Z","timestamp":1588929090000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-47436-2_62"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030474355","9783030474362"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-47436-2_62","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":"6 May 2020","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"11 May 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2020.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":"CMT System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"628","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":"135","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":"3-4","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":"6-8","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":"The conference was held 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}