{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:30:10Z","timestamp":1750221010422,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"AUFF","award":["28209"],"award-info":[{"award-number":["28209"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330893","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"686-695","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Figuring out the User in a Few Steps"],"prefix":"10.1145","author":[{"given":"Nikita","family":"Klyuchnikov","sequence":"first","affiliation":[{"name":"Skoltech, Moscow, Russian Fed."}]},{"given":"Davide","family":"Mottin","sequence":"additional","affiliation":[{"name":"Aarhus University, Aarhus, Denmark"}]},{"given":"Georgia","family":"Koutrika","sequence":"additional","affiliation":[{"name":"Athena Research and Innovation Center, Athens, Greece"}]},{"given":"Emmanuel","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"University of Bonn, Bonn, Germany"}]},{"given":"Panagiotis","family":"Karras","sequence":"additional","affiliation":[{"name":"Aarhus University, Aarhus, Denmark"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01197433"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Evgeny Burnaev and Maxim Panov. 2015. Adaptive design of experiments based on Gaussian processes. SLDS. 116--125.  Evgeny Burnaev and Maxim Panov. 2015. Adaptive design of experiments based on Gaussian processes. SLDS. 116--125.","DOI":"10.1007\/978-3-319-17091-6_7"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.22.1.222"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273523"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"key":"e_1_3_2_1_6_1","volume-title":"Proc. KDD Cup","author":"Dror Gideon","year":"2012","unstructured":"Gideon Dror , Noam Koenigstein , Yehuda Koren , and Markus Weimer . 2012 . The Yahoo! music dataset and KDD-Cup '11 . In Proc. KDD Cup 2011. 8--18. Gideon Dror, Noam Koenigstein, Yehuda Koren, and Markus Weimer. 2012. The Yahoo! music dataset and KDD-Cup '11. In Proc. KDD Cup 2011. 8--18."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2007.1900"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.2514\/1.12466"},{"key":"e_1_3_2_1_9_1","volume-title":"Mann","author":"Garnett Roman","year":"2012","unstructured":"Roman Garnett , Yamuna Krishnamurthy , Xuehan Xiong , Jeff G. Schneider , and Richard P . Mann . 2012 . Bayesian optimal active search and surveying. In ICML . Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff G. Schneider, and Richard P. Mann. 2012. Bayesian optimal active search and surveying. In ICML ."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_11_1","unstructured":"Yoshiharu Ishikawa Ravishankar Subramanya and Christos Faloutsos. 1998. MindReader: Querying databases through multiple examples. In VLDB. 218--227.   Yoshiharu Ishikawa Ravishankar Subramanya and Christos Faloutsos. 1998. MindReader: Querying databases through multiple examples. In VLDB. 218--227."},{"key":"e_1_3_2_1_12_1","unstructured":"Kirthevasan Kandasamy Gautam Dasarathy Junier B Oliva Jeff Schneider and Barnab\u00e1s P\u00f3czos. 2016a. Gaussian process bandit optimisation with multi-fidelity evaluations. In NIPS . 992--1000.   Kirthevasan Kandasamy Gautam Dasarathy Junier B Oliva Jeff Schneider and Barnab\u00e1s P\u00f3czos. 2016a. Gaussian process bandit optimisation with multi-fidelity evaluations. In NIPS . 992--1000."},{"key":"e_1_3_2_1_13_1","unstructured":"Kirthevasan Kandasamy Gautam Dasarathy Barnabas Poczos and Jeff Schneider. 2016b. The multi-fidelity multi-armed bandit. In NIPS. 1777--1785.   Kirthevasan Kandasamy Gautam Dasarathy Barnabas Poczos and Jeff Schneider. 2016b. The multi-fidelity multi-armed bandit. In NIPS. 1777--1785."},{"key":"e_1_3_2_1_14_1","volume-title":"Wolpert","author":"Lam Remi R.","year":"2016","unstructured":"Remi R. Lam , Karen E. Willcox , and David H . Wolpert . 2016 . Bayesian optimization with a finite budget: an approximate dynamic programming approach. In NIPS. 883--891. Remi R. Lam, Karen E. Willcox, and David H. Wolpert. 2016. Bayesian optimization with a finite budget: an approximate dynamic programming approach. In NIPS. 883--891."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772758"},{"key":"e_1_3_2_1_16_1","volume-title":"Schneider","author":"Ma Yifei","year":"2015","unstructured":"Yifei Ma , Tzu-Kuo Huang , and Jeff G . Schneider . 2015 . Active search and bandits on graphs using sigma-optimality. In UAI . 542--551. Yifei Ma, Tzu-Kuo Huang, and Jeff G. Schneider. 2015. Active search and bandits on graphs using sigma-optimality. In UAI . 542--551."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0001924000006473"},{"volume-title":"Test theory: A unified treatment","author":"McDonald Roderick P","key":"e_1_3_2_1_18_1","unstructured":"Roderick P McDonald . 2013. Test theory: A unified treatment . Psychology Press . Roderick P McDonald. 2013. Test theory: A unified treatment . Psychology Press."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0601602103"},{"key":"e_1_3_2_1_20_1","unstructured":"Matthias Poloczek Jialei Wang and Peter Frazier. 2017. Multi-information source optimization. In NIPS. 4288--4298.   Matthias Poloczek Jialei Wang and Peter Frazier. 2017. Multi-information source optimization. In NIPS. 4288--4298."},{"key":"e_1_3_2_1_21_1","volume-title":"Near linear time algorithm to detect community structures in large-scale networks. Physical review E","author":"Raghavan Usha Nandini","year":"2007","unstructured":"Usha Nandini Raghavan , R\u00e9ka Albert , and Soundar Kumara . 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review E , Vol. 76 , 3 ( 2007 ), 036106. Usha Nandini Raghavan, R\u00e9ka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review E , Vol. 76, 3 (2007), 036106."},{"key":"e_1_3_2_1_22_1","volume-title":"Williams","author":"Rasmussen Carl Edward","year":"2006","unstructured":"Carl Edward Rasmussen and Christopher K. I . Williams . 2006 . Gaussian Processes for Machine Learning . , Vol. 1 . The MIT Press . Carl Edward Rasmussen and Christopher K. I. Williams. 2006. Gaussian Processes for Machine Learning . , Vol. 1. The MIT Press."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/245108.245121"},{"key":"e_1_3_2_1_24_1","unstructured":"Matthew Schultz and Thorsten Joachims. 2003. Learning a distance metric from relative comparisons. In NIPS. 41--48.   Matthew Schultz and Thorsten Joachims. 2003. Learning a distance metric from relative comparisons. In NIPS. 41--48."},{"volume-title":"Active Learning","author":"Settles Burr","key":"e_1_3_2_1_25_1","unstructured":"Burr Settles . 2012. Active Learning . Morgan & Claypool Publishers . Burr Settles. 2012. Active Learning . Morgan & Claypool Publishers."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2182033"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/500141.500159"},{"key":"e_1_3_2_1_28_1","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten Laurens J. P.","year":"2008","unstructured":"Laurens J. P. van der Maaten and Geoffrey E. Hinton . 2008 . Visualizing data using t-SNE . JMLR , Vol. 9 (2008), 2579 -- 2605 . Laurens J. P. van der Maaten and Geoffrey E. Hinton. 2008. Visualizing data using t-SNE. JMLR , Vol. 9 (2008), 2579--2605.","journal-title":"JMLR"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783360"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487605"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-017-9545-y"},{"key":"e_1_3_2_1_32_1","unstructured":"Alexey Zaytsev and Evgeny Burnaev. 2017b. Minimax approach to variable fidelity data interpolation. In AISTATS . 652--661.  Alexey Zaytsev and Evgeny Burnaev. 2017b. Minimax approach to variable fidelity data interpolation. In AISTATS . 652--661."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-STS518"},{"key":"e_1_3_2_1_34_1","volume-title":"NIPS Workshop on Bayesian Optimization .","author":"Zhang Yehong","year":"2017","unstructured":"Yehong Zhang , Trong Nghia Hoang , Bryan Kian Hsiang Low , and Mohan Kankanhalli . 2017 . Information-based multi-fidelity Bayesian optimization . In NIPS Workshop on Bayesian Optimization . Yehong Zhang, Trong Nghia Hoang, Bryan Kian Hsiang Low, and Mohan Kankanhalli. 2017. Information-based multi-fidelity Bayesian optimization. In NIPS Workshop on Bayesian Optimization ."}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Anchorage AK USA","acronym":"KDD '19"},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330893","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330893","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:03Z","timestamp":1750206363000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330893"}},"subtitle":["Bayesian Multifidelity Active Search with Cokriging"],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":34,"alternative-id":["10.1145\/3292500.3330893","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330893","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}