{"id":"https://openalex.org/W4416018564","doi":"https://doi.org/10.48550/arxiv.2511.03125","title":"Provable Accelerated Bayesian Optimization with Knowledge Transfer","display_name":"Provable Accelerated Bayesian Optimization with Knowledge Transfer","publication_year":2025,"publication_date":"2025-11-05","ids":{"openalex":"https://openalex.org/W4416018564","doi":"https://doi.org/10.48550/arxiv.2511.03125"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2511.03125","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.03125","pdf_url":"https://arxiv.org/pdf/2511.03125","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","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.03125","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100308160","display_name":"Haitao Lin","orcid":"https://orcid.org/0009-0003-7627-3718","observed_orcids":["https://orcid.org/0009-0003-7627-3718"]},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin, Haitao","raw_affiliation_strings":["Department of Statistics , University of Chicago , Chicago , IL , USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , University of Chicago , Chicago , IL , USA,","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046388151","display_name":"Boxin Zhao","orcid":"https://orcid.org/0000-0003-4739-1339","observed_orcids":["https://orcid.org/0000-0003-4739-1339"]},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhao, Boxin","raw_affiliation_strings":["University of Chicago Booth School of Business , Chicago , IL , USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago Booth School of Business , Chicago , IL , USA,","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016868056","display_name":"Mladen Kolar","orcid":"https://orcid.org/0000-0001-7353-3404","observed_orcids":["https://orcid.org/0000-0001-7353-3404"]},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I1327163397","display_name":"State University of New York","ror":"https://ror.org/01q1z8k08","country_code":"US","type":"education","lineage":["https://openalex.org/I1327163397"]},{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kolar, Mladen","raw_affiliation_strings":["Department of Computer Science , University at Albany , State University of New York , Albany , NY , USA,","Department of Data Sciences and Operations , University of Southern California Marshall School of Business , Los Angeles , CA , USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science , University at Albany , State University of New York , Albany , NY , USA,","institution_ids":["https://openalex.org/I1327163397","https://openalex.org/I392282"]},{"raw_affiliation_string":"Department of Data Sciences and Operations , University of Southern California Marshall School of Business , Los Angeles , CA , USA,","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115600525","display_name":"Chong Liu","orcid":"https://orcid.org/0009-0008-1673-6422","observed_orcids":["https://orcid.org/0009-0008-1673-6422"]},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Liu, Chong","raw_affiliation_strings":["Department of Statistics and Data Science , Mohamed bin Zayed University of Artificial Intelligence , Masdar City , Abu Dhabi , UAE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science , Mohamed bin Zayed University of Artificial Intelligence , Masdar City , Abu Dhabi , UAE","institution_ids":["https://openalex.org/I4210113480"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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":0.9994999766349792,"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":0.9994999766349792,"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":0.0,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.9470000267028809},{"id":"https://openalex.org/keywords/knowledge-transfer","display_name":"knowledge transfer","score":0.9760000109672546},{"id":"https://openalex.org/keywords/accelerated-optimization","display_name":"accelerated optimization","score":0.6159999966621399},{"id":"https://openalex.org/keywords/regret-bounds","display_name":"regret bounds","score":0.6880000233650208},{"id":"https://openalex.org/keywords/difference-function","display_name":"difference function","score":0.7110000252723694},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"uncertainty quantification","score":0.9520000219345093},{"id":"https://openalex.org/keywords/reproducing-kernel-hilbert-space","display_name":"reproducing kernel Hilbert space","score":0.9520000219345093},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.753000020980835},{"id":"https://openalex.org/keywords/information-gain","display_name":"information gain","score":0.47999998927116394}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.8004999756813049},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5922999978065491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5843999981880188},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5641000270843506},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5634999871253967},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5591999888420105},{"id":"https://openalex.org/C80884492","wikidata":"https://www.wikidata.org/wiki/Q3345678","display_name":"Reproducing kernel Hilbert space","level":3,"score":0.5299999713897705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5202999711036682},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.48669999837875366},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46059998869895935},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37770000100135803},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3319000005722046},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3312000036239624},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2935999929904938},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C62799726","wikidata":"https://www.wikidata.org/wiki/Q190056","display_name":"Hilbert space","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.27250000834465027},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2646999955177307},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2511.03125","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.03125","pdf_url":"https://arxiv.org/pdf/2511.03125","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":"doi:10.48550/arxiv.2511.03125","doi":"https://doi.org/10.48550/arxiv.2511.03125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.03125","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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.03125","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.03125","pdf_url":"https://arxiv.org/pdf/2511.03125","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":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416018564.pdf","grobid_xml":"https://content.openalex.org/works/W4416018564.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"study":[1],"how":[2],"to":[3,89,91],"accelerate":[4],"Bayesian":[5],"optimization":[6],"(BO)":[7],"on":[8,22,75,151,176],"a":[9,71],"target":[10,53,84,135],"task":[11],"by":[12],"transferring":[13],"historical":[14],"knowledge":[15,25],"from":[16,122],"related":[17],"source":[18,82,123,133],"tasks.":[19],"Existing":[20],"work":[21],"BO":[23,37],"with":[24],"transfer":[26],"either":[27],"lacks":[28],"theoretical":[29,170],"guarantees":[30],"or":[31],"achieves":[32],"the":[33,39,47,52,66,76,81,104,118],"same":[34],"regret":[35,105],"as":[36],"in":[38],"non-transfer":[40],"setting,":[41],"$\\widetilde{O}(\\sqrt{T":[42,111],"\u03b3_f})$,":[43],"where":[44,115],"$T$":[45],"is":[46,108,174],"number":[48,119],"of":[49,51,106,109,120],"evaluations":[50,121],"function":[54,78],"and":[55,83,125,134,156,166],"$\u03b3_f$":[56],"denotes":[57,117],"its":[58],"information":[59],"gain.":[60],"In":[61,130],"this":[62],"paper,":[63],"we":[64,101],"propose":[65],"DeltaBO":[67,107,161],"algorithm,":[68],"which":[69,86,139],"builds":[70],"novel":[72],"uncertainty-quantification":[73],"approach":[74],"difference":[77],"$\u03b4$":[79],"between":[80],"functions,":[85],"are":[87,137],"allowed":[88],"belong":[90],"different":[92],"Reproducing":[93],"Kernel":[94],"Hilbert":[95],"Spaces":[96],"(RKHSs).":[97],"Under":[98],"mild":[99],"assumptions,":[100],"prove":[102],"that":[103,141,160],"order":[110],"(T/N":[112],"+":[113],"\u03b3_\u03b4)})$,":[114],"$N$":[116],"tasks":[124,136,155],"typically":[126],"$N":[127],"\\gg":[128],"T$.":[129],"many":[131],"applications,":[132],"similar,":[138],"implies":[140],"$\u03b3_\u03b4$":[142],"can":[143],"be":[144],"much":[145],"smaller":[146],"than":[147],"$\u03b3_f$.":[148],"Empirical":[149],"studies":[150],"both":[152],"real-world":[153],"hyperparameter-tuning":[154],"synthetic":[157],"functions":[158],"show":[159],"outperforms":[162],"other":[163],"baseline":[164],"methods":[165],"also":[167],"verify":[168],"our":[169],"claims.":[171],"Our":[172],"code":[173],"available":[175],"GitHub.":[177]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-11-07T00:00:00"}
