{"id":"https://openalex.org/W4372273129","doi":"https://doi.org/10.48550/arxiv.2305.02614","title":"High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling","display_name":"High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling","publication_year":2023,"publication_date":"2023-05-04","ids":{"openalex":"https://openalex.org/W4372273129","doi":"https://doi.org/10.48550/arxiv.2305.02614"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.02614","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02614","pdf_url":"https://arxiv.org/pdf/2305.02614","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.02614","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111061946","display_name":"Yuxuan Yin","orcid":"https://orcid.org/0009-0001-5504-3142","observed_orcids":["https://orcid.org/0009-0001-5504-3142"]},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yin, Yuxuan","raw_affiliation_strings":["University of California, Santa Barbara"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445217","display_name":"Yu Wang","orcid":"https://orcid.org/0000-0002-4788-8655","observed_orcids":["https://orcid.org/0000-0002-4788-8655"]},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wang, Yu","raw_affiliation_strings":["University of California, Santa Barbara"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432795","display_name":"Peng Li","orcid":"https://orcid.org/0000-0003-4981-0496","observed_orcids":["https://orcid.org/0000-0003-4981-0496"]},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li, Peng","raw_affiliation_strings":["University of California, Santa Barbara"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara","institution_ids":["https://openalex.org/I154570441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154570441"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9721999764442444,"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":0.9721999764442444,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.008799999952316284,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.004900000058114529,"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/bayesian-optimization","display_name":"Bayesian optimization","score":0.9340000152587891},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.8809999823570251},{"id":"https://openalex.org/keywords/pseudo-labeling","display_name":"pseudo-labeling","score":0.8870000243186951},{"id":"https://openalex.org/keywords/kriging-surrogate-model","display_name":"Kriging surrogate model","score":0.6470000147819519},{"id":"https://openalex.org/keywords/sample-efficiency","display_name":"sample efficiency","score":0.6819999814033508},{"id":"https://openalex.org/keywords/high-dimensional-optimization","display_name":"high-dimensional optimization","score":0.7409999966621399},{"id":"https://openalex.org/keywords/global-optimization","display_name":"global optimization","score":0.6340000033378601}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8253047466278076},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.8170002698898315},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6833542585372925},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6564412713050842},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.627933144569397},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6019425392150879},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5681872367858887},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5025761127471924},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46549853682518005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4525827169418335},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4325682520866394},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4119420647621155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40747523307800293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18019792437553406},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0889265239238739},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2305.02614","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02614","pdf_url":"https://arxiv.org/pdf/2305.02614","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"pmh:oai:osti.gov:2483643","doi":null,"is_oa":false,"landing_page_url":"https://www.osti.gov/biblio/2483643","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},{"id":"doi:10.48550/arxiv.2305.02614","doi":"https://doi.org/10.48550/arxiv.2305.02614","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.02614","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.02614","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02614","pdf_url":"https://arxiv.org/pdf/2305.02614","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4372273129.pdf","grobid_xml":"https://content.openalex.org/works/W4372273129.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1480330138","https://openalex.org/W1560021816","https://openalex.org/W1665214252","https://openalex.org/W1719489212","https://openalex.org/W1757990252","https://openalex.org/W1959608418","https://openalex.org/W1991788899","https://openalex.org/W2025720061","https://openalex.org/W2061144551","https://openalex.org/W2099201756","https://openalex.org/W2117154949","https://openalex.org/W2122456939","https://openalex.org/W2529996553","https://openalex.org/W2560321925","https://openalex.org/W2592691248","https://openalex.org/W2736941579","https://openalex.org/W2786722833","https://openalex.org/W2804368052","https://openalex.org/W2809657535","https://openalex.org/W2873705236","https://openalex.org/W2900070061","https://openalex.org/W2900694120","https://openalex.org/W2903155537","https://openalex.org/W2943865428","https://openalex.org/W2945677399","https://openalex.org/W2946928580","https://openalex.org/W2947035977","https://openalex.org/W2950183588","https://openalex.org/W2951970475","https://openalex.org/W2952229419","https://openalex.org/W2962764565","https://openalex.org/W2963676163","https://openalex.org/W2964159205","https://openalex.org/W2969999645","https://openalex.org/W3003878737","https://openalex.org/W3030501230","https://openalex.org/W3035160371","https://openalex.org/W3099570996","https://openalex.org/W3099760900","https://openalex.org/W3106031848","https://openalex.org/W3124229194","https://openalex.org/W3133755701","https://openalex.org/W3166028936","https://openalex.org/W3205500251","https://openalex.org/W3208420775","https://openalex.org/W3213435748","https://openalex.org/W4211049957","https://openalex.org/W4221153814","https://openalex.org/W4226177482","https://openalex.org/W4318348348","https://openalex.org/W4366999506","https://openalex.org/W4393147410","https://openalex.org/W7133215130","https://openalex.org/W7133216281"],"related_works":["https://openalex.org/W4393147410","https://openalex.org/W4378498829","https://openalex.org/W4282920007","https://openalex.org/W3030501230","https://openalex.org/W2969999645","https://openalex.org/W3166028936","https://openalex.org/W4415428380","https://openalex.org/W4380558509","https://openalex.org/W4292388060","https://openalex.org/W3124357085"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,30,38,67],"novel":[3],"semi-supervised":[4],"learning":[5],"approach,":[6],"named":[7],"Teacher-Student":[8],"Bayesian":[9,125],"Optimization":[10],"($\\texttt{TSBO}$),":[11],"integrating":[12],"the":[13,25,51,58,72,75,83,91,94,99,122,132,135,139,145,148],"teacher-student":[14,136],"paradigm":[15],"into":[16],"BO":[17],"to":[18,71,85,113,144],"minimize":[19],"expensive":[20],"labeled":[21,166],"data":[22,35,47,111,167],"queries":[23],"for":[24,138],"first":[26],"time.":[27],"$\\texttt{TSBO}$":[28,154],"incorporates":[29],"teacher":[31,73,84,146],"model,":[32],"an":[33],"unlabeled":[34,46,110],"sampler,":[36,52],"and":[37,130],"student":[39,42,78,116],"model.":[40],"The":[41,60],"is":[43],"trained":[44],"on":[45],"locations":[48],"generated":[49],"by":[50,57],"with":[53,121],"pseudo":[54,88],"labels":[55],"predicted":[56],"teacher.":[59],"interplay":[61],"between":[62],"these":[63],"three":[64],"components":[65],"implements":[66],"unique":[68],"selective":[69],"regularization":[70],"in":[74,98,147,159],"form":[76],"of":[77,93,124,134,141,150],"feedback.":[79],"This":[80],"scheme":[81],"enables":[82],"predict":[86],"high-quality":[87],"labels,":[89],"enhancing":[90],"generalization":[92],"GP":[95],"surrogate":[96],"model":[97,137],"search":[100],"space.":[101],"To":[102],"fully":[103],"exploit":[104],"$\\texttt{TSBO}$,":[105],"we":[106,128],"propose":[107],"two":[108],"optimized":[109],"samplers":[112],"construct":[114],"effective":[115],"feedback":[117,143],"that":[118],"well":[119],"aligns":[120],"objective":[123],"optimization.":[126],"Furthermore,":[127],"quantify":[129],"leverage":[131],"uncertainty":[133],"provision":[140],"reliable":[142],"presence":[149],"risky":[151],"pseudo-label":[152],"predictions.":[153],"demonstrates":[155],"significantly":[156],"improved":[157],"sample-efficiency":[158],"several":[160],"global":[161],"optimization":[162],"tasks":[163],"under":[164],"tight":[165],"budgets.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-05-07T00:00:00"}
