{"id":"https://openalex.org/W3065385383","doi":"https://doi.org/10.6094/unifr/166637","title":"Efficient bayesian hyperparameter optimization","display_name":"Efficient bayesian hyperparameter optimization","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3065385383","doi":"https://doi.org/10.6094/unifr/166637","mag":"3065385383"},"language":"en","primary_location":{"id":"doi:10.6094/unifr/166637","is_oa":true,"landing_page_url":"https://doi.org/10.6094/unifr/166637","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"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"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.6094/unifr/166637","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044806693","display_name":"Aaron Klein","orcid":"https://orcid.org/0000-0003-3960-7742"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Klein, Aaron","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5044806693"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9751999974250793,"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":0.9751999974250793,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9535999894142151,"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":0.9193000197410583,"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":0.7921484708786011},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5622401237487793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5148398280143738},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5002841949462891},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.491197794675827},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.427796334028244},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3350140154361725},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31450045108795166}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7921484708786011},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5622401237487793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5148398280143738},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5002841949462891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.491197794675827},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.427796334028244},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3350140154361725},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31450045108795166}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.6094/unifr/166637","is_oa":true,"landing_page_url":"https://doi.org/10.6094/unifr/166637","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"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"},{"id":"mag:3065385383","is_oa":false,"landing_page_url":"https://d-nb.info/1214592961/34","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.6094/unifr/166637","is_oa":true,"landing_page_url":"https://doi.org/10.6094/unifr/166637","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2493506256","https://openalex.org/W2731743994"],"abstract_inverted_index":{"Automated":[0],"machine":[1,11,22,47,268],"learning":[2,12,23,48,225,264,269],"emerged":[3],"as":[4,224,272],"a":[5,45,102,148,256],"new":[6,149,214,257,290],"research":[7],"field":[8],"inside":[9,189],"of":[10,20,32,44,85,105,163,177,199,238,243,266,282,289,314,333],"that":[13,157,218,299,310],"tries":[14],"to":[15,56,61,77,118,133,137,159,209,230,277,322,327],"progressively":[16],"automate":[17],"different":[18,167,335],"steps":[19],"common":[21],"pipelines":[24],"which":[25,50,109,185,205,261],"are":[26,186,203,206],"traditionally":[27,187],"executed":[28],"by":[29,241,307],"humans.":[30],"One":[31,84],"its":[33,97,139],"core":[34],"tasks":[35,168],"is":[36,53,67,93,116,293],"the":[37,41,81,86,106,120,123,161,175,200,232,263,278,312,315,340],"automated":[38],"search":[39,121,234],"for":[40,90,141],"right":[42],"hyperparameters":[43],"given":[46],"algorithm":[49],"in":[51,72,236],"practice":[52],"often":[54,195,294],"essential":[55],"achieve":[57],"good":[58],"performance.":[59],"Compare":[60],"other":[62],"optimization":[63,66,92,100,136,143,216,337],"problems,":[64],"hyperparameter":[65,91,142,164,193,283,336],"usually":[68],"particularly":[69],"expensive,":[70],"since":[71],"each":[73],"iteration,":[74],"it":[75],"requires":[76],"train":[78],"and":[79,169,180,287,304,331],"validate":[80],"underlying":[82],"algorithm.":[83],"most":[87],"successful":[88],"approaches":[89],"Bayesian":[94,99,135,154,190,215,250],"optimization.":[95,191],"At":[96],"core,":[98],"fits":[101],"probabilistic":[103,150],"model":[104,151,160],"objective":[107,201],"function,":[108],"together":[110],"with":[111,174],"an":[112],"additional":[113],"acquisition":[114],"function":[115,202],"used":[117,188],"guide":[119],"towards":[122],"global":[124],"optimum.":[125],"In":[126,192],"this":[127],"thesis":[128],"we":[129,146,254],"present":[130,212,255],"several":[131],"extensions":[132],"standard":[134],"improve":[138,231],"performance":[140,162],"problems.":[144],"First,":[145],"introduce":[147],"based":[152,246],"on":[153,247],"neural":[155,251,258,273],"networks,":[156],"allows":[158,325],"configurations":[165],"across":[166],"thereby":[170],"scales":[171],"much":[172,207],"better":[173],"number":[176],"data":[178],"points":[179],"dimensions":[181],"than":[182],"Gaussian":[183],"processes":[184],"optimization,":[194,284],"approximations,":[196],"so-called":[197],"fidelities,":[198,222],"available":[204],"cheaper":[208],"evaluate.":[210,323],"We":[211,297],"two":[213],"methods":[217,292,338],"can":[219,301],"leverage":[220],"such":[221,223,271],"curves":[226],"or":[227],"dataset":[228],"subsets,":[229],"overall":[233],"process":[235],"terms":[237],"wall-clock":[239],"time":[240],"orders":[242],"magnitude.":[244],"Furthermore,":[245],"our":[248],"proposed":[249],"network":[252,259],"model,":[253],"architecture":[260],"models":[262],"curve":[265],"iterative":[267],"methods,":[270],"networks.":[274],"Finally,":[275],"due":[276],"high":[279],"computational":[280],"cost":[281],"thorough":[285],"benchmarking":[286],"evaluation":[288],"developed":[291],"prohibitively":[295],"expensive.":[296],"show":[298],"one":[300],"approximate":[302],"continuous":[303],"discrete":[305],"benchmarks":[306,309],"surrogate":[308],"capture":[311],"characteristics":[313],"original":[316],"benchmark":[317],"but":[318],"take":[319],"only":[320],"milliseconds":[321],"This":[324],"us":[326],"performa":[328],"rigorous":[329],"analysis":[330],"comparison":[332],"various":[334],"from":[339],"literature.":[341]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
