{"id":"https://openalex.org/W4388963713","doi":"https://doi.org/10.48550/arxiv.2311.13460","title":"Multi-Objective Bayesian Optimization with Active Preference Learning","display_name":"Multi-Objective Bayesian Optimization with Active Preference Learning","publication_year":2023,"publication_date":"2023-11-22","ids":{"openalex":"https://openalex.org/W4388963713","doi":"https://doi.org/10.48550/arxiv.2311.13460"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2311.13460","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.13460","pdf_url":"https://arxiv.org/pdf/2311.13460","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2311.13460","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064359374","display_name":"Ryota Ozaki","orcid":"https://orcid.org/0000-0002-7459-6500"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ozaki, Ryota","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006041841","display_name":"Kazuki Ishikawa","orcid":"https://orcid.org/0000-0002-2287-4820"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ishikawa, Kazuki","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111153806","display_name":"Youhei Kanzaki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kanzaki, Youhei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022704430","display_name":"Shinya Suzuki","orcid":"https://orcid.org/0000-0002-1086-3956"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suzuki, Shinya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081716738","display_name":"Shion Takeno","orcid":"https://orcid.org/0009-0000-3638-8658"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takeno, Shion","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081482638","display_name":"Ichiro Takeuchi","orcid":"https://orcid.org/0000-0002-1366-1946"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takeuchi, Ichiro","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5003668143","display_name":"Masayuki Karasuyama","orcid":"https://orcid.org/0000-0002-6177-3686"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karasuyama, Masayuki","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5064359374"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9940999746322632,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.8978191614151001},{"id":"https://openalex.org/keywords/preference-learning","display_name":"Preference learning","score":0.729158878326416},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7166634202003479},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6220811605453491},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6140618324279785},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.6059721112251282},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5775174498558044},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.5649195313453674},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4933730661869049},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49277833104133606},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4905676245689392},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.483621746301651},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42652034759521484},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4258199632167816},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19548550248146057}],"concepts":[{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.8978191614151001},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.729158878326416},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7166634202003479},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6220811605453491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140618324279785},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.6059721112251282},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5775174498558044},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.5649195313453674},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4933730661869049},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49277833104133606},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4905676245689392},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.483621746301651},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42652034759521484},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4258199632167816},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19548550248146057},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2311.13460","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.13460","pdf_url":"https://arxiv.org/pdf/2311.13460","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2311.13460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2311.13460","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2311.13460","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.13460","pdf_url":"https://arxiv.org/pdf/2311.13460","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388963713.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1515481220","https://openalex.org/W2060303324","https://openalex.org/W4206238124","https://openalex.org/W4324116389","https://openalex.org/W2161073979","https://openalex.org/W3126212998","https://openalex.org/W2516425091","https://openalex.org/W3105737538","https://openalex.org/W3093963944","https://openalex.org/W2090178682"],"abstract_inverted_index":{"There":[0],"are":[1],"a":[2,18,44,57,77],"lot":[3],"of":[4,50,81,96,156],"real-world":[5],"black-box":[6],"optimization":[7,20,59,164,168],"problems":[8,169],"that":[9],"need":[10],"to":[11,62,132],"optimize":[12],"multiple":[13],"criteria":[14],"simultaneously.":[15],"However,":[16],"in":[17,34,68,75,116,121],"multi-objective":[19],"(MOO)":[21],"problem,":[22],"identifying":[23,63],"the":[24,29,38,48,51,64,69,82,93,99,107,118,122,126,134,138,148,154,161,166],"whole":[25],"Pareto":[26,52],"front":[27],"requires":[28],"prohibitive":[30],"search":[31],"cost,":[32],"while":[33],"many":[35],"practical":[36],"scenarios,":[37],"decision":[39],"maker":[40],"(DM)":[41],"only":[42],"needs":[43],"specific":[45],"solution":[46,67],"among":[47],"set":[49],"optimal":[53],"solutions.":[54],"We":[55,151],"propose":[56,142],"Bayesian":[58,78],"(BO)":[60],"approach":[61],"most":[65,108],"preferred":[66,109],"MOO":[70],"with":[71,137],"expensive":[72],"objective":[73,123],"functions,":[74],"which":[76,117],"preference":[79,101,128,149],"model":[80],"DM":[83,127],"is":[84,129],"adaptively":[85],"estimated":[86],"by":[87],"an":[88,113,143],"interactive":[89],"manner":[90],"based":[91],"on":[92],"two":[94],"types":[95],"supervisions":[97],"called":[98],"pairwise":[100],"and":[102,125,165],"improvement":[103],"request.":[104],"To":[105],"explore":[106],"solution,":[110],"we":[111,140],"define":[112],"acquisition":[114],"function":[115,163],"uncertainty":[119],"both":[120],"functions":[124],"incorporated.":[130],"Further,":[131],"minimize":[133],"interaction":[135],"cost":[136],"DM,":[139],"also":[141],"active":[144],"learning":[145,172],"strategy":[146],"for":[147,170],"estimation.":[150],"empirically":[152],"demonstrate":[153],"effectiveness":[155],"our":[157],"proposed":[158],"method":[159],"through":[160],"benchmark":[162],"hyper-parameter":[167],"machine":[171],"models.":[173]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
