{"id":"https://openalex.org/W4281874897","doi":"https://doi.org/10.1145/3534678.3539369","title":"Transfer Learning based Search Space Design for Hyperparameter Tuning","display_name":"Transfer Learning based Search Space Design for Hyperparameter Tuning","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4281874897","doi":"https://doi.org/10.1145/3534678.3539369"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539369","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539369","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2206.02511","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100421644","display_name":"Yang Li","orcid":"https://orcid.org/0000-0002-8381-7272"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Li","raw_affiliation_strings":["Peking University &amp; Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659","https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100770388","display_name":"Yu Shen","orcid":"https://orcid.org/0000-0001-6503-6504"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Shen","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086112587","display_name":"Huaijun Jiang","orcid":"https://orcid.org/0000-0003-1566-4849"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaijun Jiang","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102781208","display_name":"Tianyi Bai","orcid":"https://orcid.org/0009-0009-5057-7100"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyi Bai","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100459878","display_name":"Wentao Zhang","orcid":"https://orcid.org/0000-0003-4549-7498"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Zhang","raw_affiliation_strings":["Peking University &amp; Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659","https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004750246","display_name":"Ce Zhang","orcid":"https://orcid.org/0000-0001-5100-3584"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Ce Zhang","raw_affiliation_strings":["ETH Z\u00fcrich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Z\u00fcrich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062357883","display_name":"Bin Cui","orcid":"https://orcid.org/0000-0003-1681-4677"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Cui","raw_affiliation_strings":["Peking University &amp; Peking University (Qingdao), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University &amp; Peking University (Qingdao), Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100421644"],"corresponding_institution_ids":["https://openalex.org/I20231570","https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":0.733,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.70387449,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"967","last_page":"977"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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":0.9977999925613403,"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":0.9966999888420105,"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/hyperparameter","display_name":"Hyperparameter","score":0.8778233528137207},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.756644070148468},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.6892680525779724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6829335689544678},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.659202516078949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6297656893730164},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.520327091217041},{"id":"https://openalex.org/keywords/universality","display_name":"Universality (dynamical systems)","score":0.49772313237190247}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8778233528137207},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.756644070148468},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.6892680525779724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6829335689544678},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.659202516078949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6297656893730164},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.520327091217041},{"id":"https://openalex.org/C183992945","wikidata":"https://www.wikidata.org/wiki/Q2495574","display_name":"Universality (dynamical systems)","level":2,"score":0.49772313237190247},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3534678.3539369","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539369","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2206.02511","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.02511","pdf_url":"https://arxiv.org/pdf/2206.02511","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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.02511","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.02511","pdf_url":"https://arxiv.org/pdf/2206.02511","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4253300795","display_name":null,"funder_award_id":"61832001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W76331760","https://openalex.org/W122178443","https://openalex.org/W1500474910","https://openalex.org/W1502922572","https://openalex.org/W1510052597","https://openalex.org/W1575029535","https://openalex.org/W1640798781","https://openalex.org/W2097998348","https://openalex.org/W2106411961","https://openalex.org/W2113145584","https://openalex.org/W2131241448","https://openalex.org/W2132862423","https://openalex.org/W2160815625","https://openalex.org/W2165698076","https://openalex.org/W2182361439","https://openalex.org/W2192203593","https://openalex.org/W2194775991","https://openalex.org/W2200000192","https://openalex.org/W2352092692","https://openalex.org/W2507221917","https://openalex.org/W2510167369","https://openalex.org/W2556372419","https://openalex.org/W2556522401","https://openalex.org/W2732547613","https://openalex.org/W2765318529","https://openalex.org/W2785444061","https://openalex.org/W2798650501","https://openalex.org/W2891146651","https://openalex.org/W2896457183","https://openalex.org/W2950220059","https://openalex.org/W2950277768","https://openalex.org/W2963248505","https://openalex.org/W2965658867","https://openalex.org/W2998125499","https://openalex.org/W2998993395","https://openalex.org/W3096437212","https://openalex.org/W3100008240","https://openalex.org/W3100055683","https://openalex.org/W3100203766","https://openalex.org/W3111140035","https://openalex.org/W3153413104","https://openalex.org/W3167899070","https://openalex.org/W3171733623","https://openalex.org/W3179950556","https://openalex.org/W3185616770","https://openalex.org/W3188798563","https://openalex.org/W3194119111","https://openalex.org/W3195384110","https://openalex.org/W4212774754","https://openalex.org/W4225959071","https://openalex.org/W4283322925","https://openalex.org/W4283331987","https://openalex.org/W4286970057","https://openalex.org/W4287907702","https://openalex.org/W4288102071","https://openalex.org/W4289763996","https://openalex.org/W4324106947","https://openalex.org/W4392271976","https://openalex.org/W6600175266"],"related_works":["https://openalex.org/W4233965824","https://openalex.org/W2607442583","https://openalex.org/W3199199693","https://openalex.org/W4286970057","https://openalex.org/W2967237190","https://openalex.org/W3028852288","https://openalex.org/W4323366554","https://openalex.org/W2405673391","https://openalex.org/W4292388060","https://openalex.org/W2200000192"],"abstract_inverted_index":{"The":[0,110],"tuning":[1,39,76,150],"of":[2,75,129,142],"hyperparameters":[3,32],"becomes":[4],"increasingly":[5],"important":[6],"as":[7],"machine":[8,145],"learning":[9,97,146,149],"(ML)":[10],"models":[11],"have":[12],"been":[13,45],"extensively":[14],"applied":[15],"in":[16,41,48],"data":[17],"mining":[18],"applications.":[19],"Among":[20],"various":[21],"approaches,":[22],"Bayesian":[23],"optimization":[24],"(BO)":[25],"is":[26],"a":[27,122,139],"successful":[28],"methodology":[29],"to":[30,66,89],"tune":[31],"automatically.":[33],"While":[34],"traditional":[35],"methods":[36,94],"optimize":[37],"each":[38],"task":[40],"isolation,":[42],"there":[43],"has":[44],"recent":[46],"interest":[47],"speeding":[49],"up":[50],"BO":[51,69,93,119],"by":[52,120],"transferring":[53],"knowledge":[54],"across":[55],"previous":[56],"tasks.":[57,80],"In":[58,99],"this":[59],"work,":[60],"we":[61],"introduce":[62],"an":[63],"automatic":[64],"method":[65],"design":[67],"the":[68,73,103,131,136],"search":[70,126],"space":[71,127],"with":[72,95],"aid":[74],"history":[77],"from":[78],"past":[79],"This":[81],"simple":[82],"yet":[83],"effective":[84],"approach":[85,116],"can":[86],"be":[87],"used":[88],"endow":[90],"many":[91],"existing":[92],"transfer":[96],"capabilities.":[98],"addition,":[100],"it":[101],"enjoys":[102],"three":[104],"advantages:":[105],"universality,":[106],"generality,":[107],"and":[108,124,134,147,152],"safeness.":[109],"extensive":[111],"experiments":[112],"show":[113],"that":[114],"our":[115],"considerably":[117],"boosts":[118],"designing":[121],"promising":[123],"compact":[125],"instead":[128],"using":[130],"entire":[132],"space,":[133],"outperforms":[135],"state-of-the-arts":[137],"on":[138],"wide":[140],"range":[141],"benchmarks,":[143],"including":[144],"deep":[148],"tasks,":[151],"neural":[153],"architecture":[154],"search.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
