{"id":"https://openalex.org/W2134869213","doi":"https://doi.org/10.5555/1953048.2078200","title":"Hierarchical Knowledge Gradient for Sequential Sampling","display_name":"Hierarchical Knowledge Gradient for Sequential Sampling","publication_year":2011,"publication_date":"2011-02-01","ids":{"openalex":"https://openalex.org/W2134869213","doi":"https://doi.org/10.5555/1953048.2078200","mag":"2134869213"},"language":"en","primary_location":{"id":"pmh:oai:ris.utwente.nl:publications/3de42b0a-c8c3-4c26-ab82-1835eb54fd97","is_oa":true,"landing_page_url":"http://jmlr.csail.mit.edu/papers/v12/mes11a.html","pdf_url":"http://jmlr.org/papers/volume12/mes11a/mes11a.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mes, M R K, Powell, W B & Frazier, P I 2011, 'Hierarchical Knowledge Gradient for Sequential Sampling', Journal of machine learning research, vol. 12, no. 10, pp. 2931-2974. < http://jmlr.csail.mit.edu/papers/v12/mes11a.html >","raw_type":"info:eu-repo/semantics/article"},"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://jmlr.org/papers/volume12/mes11a/mes11a.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005906246","display_name":"Martijn Mes","orcid":"https://orcid.org/0000-0001-9676-5259"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Martijn Mes","raw_affiliation_strings":["Univ. of Twente#TAB#"],"raw_orcid":"https://orcid.org/0000-0001-9676-5259","affiliations":[{"raw_affiliation_string":"Univ. of Twente#TAB#","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054833334","display_name":"Warren B. Powell","orcid":"https://orcid.org/0000-0002-4364-7555"},"institutions":[{"id":"https://openalex.org/I4210094144","display_name":"Environmental Institute","ror":"https://ror.org/00n893e13","country_code":"SK","type":"company","lineage":["https://openalex.org/I4210094144"]}],"countries":["SK"],"is_corresponding":false,"raw_author_name":"Warren B. Powell","raw_affiliation_strings":["Princeton Environmental Institute"],"raw_orcid":"https://orcid.org/0000-0002-4364-7555","affiliations":[{"raw_affiliation_string":"Princeton Environmental Institute","institution_ids":["https://openalex.org/I4210094144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039019367","display_name":"Peter I. Frazier","orcid":"https://orcid.org/0000-0002-3501-3341"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter I. Frazier","raw_affiliation_strings":["Cornell University"],"raw_orcid":"https://orcid.org/0000-0002-3501-3341","affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":15.4271,"has_fulltext":true,"cited_by_count":92,"citation_normalized_percentile":{"value":0.99202173,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"12","issue":"10","first_page":"2931","last_page":"2974"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7203714847564697},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6893368363380432},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.588467001914978},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5719907283782959},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5615362524986267},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5546004772186279},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.5159392952919006},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5012609958648682},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48809850215911865},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4618920385837555},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4425256848335266},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4224618077278137},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38549238443374634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3619345426559448},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2244916558265686},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09533476829528809}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7203714847564697},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6893368363380432},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.588467001914978},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5719907283782959},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5615362524986267},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5546004772186279},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.5159392952919006},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5012609958648682},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48809850215911865},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4618920385837555},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4425256848335266},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4224618077278137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38549238443374634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3619345426559448},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2244916558265686},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09533476829528809},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":12,"locations":[{"id":"pmh:oai:ris.utwente.nl:publications/3de42b0a-c8c3-4c26-ab82-1835eb54fd97","is_oa":true,"landing_page_url":"http://jmlr.csail.mit.edu/papers/v12/mes11a.html","pdf_url":"http://jmlr.org/papers/volume12/mes11a/mes11a.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mes, M R K, Powell, W B & Frazier, P I 2011, 'Hierarchical Knowledge Gradient for Sequential Sampling', Journal of machine learning research, vol. 12, no. 10, pp. 2931-2974. < http://jmlr.csail.mit.edu/papers/v12/mes11a.html >","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:653828","is_oa":false,"landing_page_url":"http://library.tue.nl/csp/dare/LinkToRepository.csp?recordnumber=653828","pdf_url":null,"source":{"id":"https://openalex.org/S4406923046","display_name":"TU/e Research Portal (Eindhoven University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"External research report"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.206.1232","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.206.1232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.castlelab.princeton.edu/Papers/MesPowellFrazier_HierarchicalKnowledgeGradient.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.208.722","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.722","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.utwente.nl/mb/ompl/staff/Mes/beta_wp290.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.228.9437","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.228.9437","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.castlelab.princeton.edu/Papers/Mes%20Powell%20Frazier%20-%20Hierarchical%20knowledge%20gradient_JMLR_Nov32011.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.415.7449","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.415.7449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume12/mes11a/mes11a.pdf","raw_type":"text"},{"id":"pmh:oai:library.tue.nl:653828","is_oa":false,"landing_page_url":"http://repository.tue.nl/653828","pdf_url":null,"source":{"id":"https://openalex.org/S4406923046","display_name":"TU/e Research Portal (Eindhoven University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"External research report"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/c175abc9-80fe-40d4-85f3-89d395ed9ffa","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/c175abc9-80fe-40d4-85f3-89d395ed9ffa","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mes, M R K, Powel, W B & Frazier, P I 2009, Hierarchical Knowledge-Gradient for Sequential Sampling. Beta working papers, no. 290, University of Twente, Research School for Operations Management and Logistics (BETA), Enschede.","raw_type":"info:eu-repo/semantics/report"},{"id":"pmh:ut:oai:ris.utwente.nl:publications/3de42b0a-c8c3-4c26-ab82-1835eb54fd97","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/3de42b0a-c8c3-4c26-ab82-1835eb54fd97","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of machine learning research, 12(10), 2931 - 2974. Microtome Publishing","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:ut:oai:ris.utwente.nl:publications/7c8f83b0-f002-4e1c-90e3-fb79a84041ba","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/hierarchical-knowledge-gradient-for-sequential-sampling(7c8f83b0-f002-4e1c-90e3-fb79a84041ba).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of machine learning research, 2011(12), 2931 - 2974","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:ut:oai:ris.utwente.nl:publications/c175abc9-80fe-40d4-85f3-89d395ed9ffa","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/hierarchical-knowledgegradient-for-sequential-sampling(c175abc9-80fe-40d4-85f3-89d395ed9ffa).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/report"},{"id":"mag:2134869213","is_oa":false,"landing_page_url":"http://castlelab.princeton.edu/Papers/MesPowellFrazier_HierarchicalKnowledgeGradient.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":{"id":"pmh:oai:ris.utwente.nl:publications/3de42b0a-c8c3-4c26-ab82-1835eb54fd97","is_oa":true,"landing_page_url":"http://jmlr.csail.mit.edu/papers/v12/mes11a.html","pdf_url":"http://jmlr.org/papers/volume12/mes11a/mes11a.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mes, M R K, Powell, W B & Frazier, P I 2011, 'Hierarchical Knowledge Gradient for Sequential Sampling', Journal of machine learning research, vol. 12, no. 10, pp. 2931-2974. < http://jmlr.csail.mit.edu/papers/v12/mes11a.html >","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2134869213.pdf","grobid_xml":"https://content.openalex.org/works/W2134869213.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W203276351","https://openalex.org/W1480376833","https://openalex.org/W1505937442","https://openalex.org/W1508384000","https://openalex.org/W1510052597","https://openalex.org/W1528133536","https://openalex.org/W1529817821","https://openalex.org/W1558516341","https://openalex.org/W1569559301","https://openalex.org/W1576452626","https://openalex.org/W1592263067","https://openalex.org/W1608015248","https://openalex.org/W1746819321","https://openalex.org/W1861050369","https://openalex.org/W1881419322","https://openalex.org/W1931792391","https://openalex.org/W1965288387","https://openalex.org/W1973885534","https://openalex.org/W1986412858","https://openalex.org/W1989780425","https://openalex.org/W1990322717","https://openalex.org/W1994616650","https://openalex.org/W2004001705","https://openalex.org/W2017031328","https://openalex.org/W2020732830","https://openalex.org/W2045656233","https://openalex.org/W2047953381","https://openalex.org/W2052514130","https://openalex.org/W2061144551","https://openalex.org/W2063180182","https://openalex.org/W2069372244","https://openalex.org/W2072302356","https://openalex.org/W2078011655","https://openalex.org/W2089425220","https://openalex.org/W2097452095","https://openalex.org/W2099201756","https://openalex.org/W2101167844","https://openalex.org/W2103581319","https://openalex.org/W2115519224","https://openalex.org/W2118075434","https://openalex.org/W2123736449","https://openalex.org/W2126774211","https://openalex.org/W2132022678","https://openalex.org/W2142447711","https://openalex.org/W2142858180","https://openalex.org/W2146879621","https://openalex.org/W2155927283","https://openalex.org/W2168405694","https://openalex.org/W2268509491","https://openalex.org/W2346138280","https://openalex.org/W2402456051","https://openalex.org/W2489939061","https://openalex.org/W2587142195","https://openalex.org/W2627076442","https://openalex.org/W2732460962","https://openalex.org/W2795495912","https://openalex.org/W2797328151","https://openalex.org/W2798261169","https://openalex.org/W2951665052"],"related_works":["https://openalex.org/W2170712470","https://openalex.org/W2161731530","https://openalex.org/W2153150459","https://openalex.org/W2135195886","https://openalex.org/W2129487943","https://openalex.org/W2128674029","https://openalex.org/W2105736392","https://openalex.org/W2104539438","https://openalex.org/W2089589302","https://openalex.org/W2008840711","https://openalex.org/W1908434430","https://openalex.org/W1858207617","https://openalex.org/W1597424211","https://openalex.org/W1589773975","https://openalex.org/W1572948250","https://openalex.org/W1548305329","https://openalex.org/W1528500442","https://openalex.org/W1521706158","https://openalex.org/W1506225902","https://openalex.org/W1487146109"],"abstract_inverted_index":{"We":[0,55,96,163],"propose":[1,97],"a":[2,22,42,57,70,98,117,171],"sequential":[3,72],"sampling":[4,73,90],"policy":[5,74,80,167],"for":[6,61],"noisy":[7],"discrete":[8],"global":[9],"optimization":[10],"and":[11,13,47,50,68,147,179],"ranking":[12],"selection,":[14],"in":[15,86,92,140],"which":[16],"we":[17],"aim":[18],"to":[19,110,160],"efficiently":[20],"explore":[21],"finite":[23],"set":[24],"of":[25,45,65,89,137,143,153],"alternatives":[26,109,114,154],"before":[27],"selecting":[28],"an":[29,144],"alternative":[30,37,67,174],"as":[31],"best":[32],"when":[33,175],"exploration":[34],"stops.":[35],"Each":[36],"may":[38],"be":[39,157],"characterized":[40],"by":[41,108],"multidimensional":[43],"vector":[44],"categorical":[46],"numerical":[48],"attributes":[49],"has":[51],"independent":[52],"normal":[53],"rewards.":[54],"use":[56],"Bayesian":[58],"probability":[59],"model":[60],"the":[62,76,83,87,104,124,135,138,141,151,161],"unknown":[63],"reward":[64],"each":[66,93],"follow":[69],"fully":[71],"called":[75],"knowledge-gradient":[77],"policy.":[78],"This":[79,120],"myopically":[81],"optimizes":[82],"expected":[84],"increment":[85],"value":[88],"information":[91],"time":[94],"period.":[95],"hierarchical":[99],"aggregation":[100,145],"technique":[101],"that":[102,155,165,183],"uses":[103],"common":[105],"features":[106],"shared":[107],"learn":[111],"about":[112],"many":[113],"from":[115],"even":[116],"single":[118],"measurement.":[119],"approach":[121],"greatly":[122],"reduces":[123],"measurement":[125],"effort":[126],"required,":[127],"but":[128],"it":[129,184],"requires":[130],"some":[131],"prior":[132],"knowledge":[133],"on":[134],"smoothness":[136],"function":[139,146],"form":[142],"computational":[148],"issues":[149],"limit":[150],"number":[152],"can":[156],"easily":[158],"considered":[159],"thousands.":[162],"prove":[164],"our":[166],"is":[168],"consistent,":[169],"finding":[170],"globally":[172],"optimal":[173],"given":[176],"enough":[177],"measurements,":[178],"show":[180],"through":[181],"simulations":[182],"performs":[185],"competitively":[186],"with":[187],"or":[188],"significantly":[189],"better":[190],"than":[191],"other":[192],"policies.":[193]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":17},{"year":2014,"cited_by_count":10},{"year":2013,"cited_by_count":10},{"year":2012,"cited_by_count":10}],"updated_date":"2025-10-10T17:16:08.811792","created_date":"2025-10-10T00:00:00"}