{"id":"https://openalex.org/W7128513551","doi":"https://doi.org/10.48550/arxiv.2602.08774","title":"Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization","display_name":"Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7128513551","doi":"https://doi.org/10.48550/arxiv.2602.08774"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.08774","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125557306","display_name":"Nicol\u00e1s Villagr\u00e1n Prieto","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Prieto, Nicol\u00e1s Villagr\u00e1n","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Garrido-Merch\u00e1n, Eduardo C.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garrido-Merch\u00e1n, Eduardo C.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5125557306"],"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/T12535","display_name":"Machine Learning and Data Classification","score":0.7684999704360962,"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":0.7684999704360962,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.09290000051259995,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.030500000342726707,"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":0.8931999802589417},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.7534000277519226},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6590999960899353},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5605000257492065},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4799000024795532},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44830000400543213},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42559999227523804},{"id":"https://openalex.org/keywords/default","display_name":"Default","score":0.4230000078678131},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.40389999747276306}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8931999802589417},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.7534000277519226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7386999726295471},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.659500002861023},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6590999960899353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6583999991416931},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5605000257492065},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4799000024795532},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C69637215","wikidata":"https://www.wikidata.org/wiki/Q702362","display_name":"Default","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.3961000144481659},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3822999894618988},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.31189998984336853},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C126661757","wikidata":"https://www.wikidata.org/wiki/Q4925641","display_name":"Random search","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2815000116825104},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.258899986743927},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2533000111579895},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.08774","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.08774","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.08774","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:doi:10.48550/arxiv.2602.08774","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Bayesian":[0],"Optimization":[1],"(BO)":[2],"is":[3,123,135],"a":[4,87],"standard":[5],"tool":[6],"for":[7,203],"hyperparameter":[8,29,211],"tuning":[9,212],"thanks":[10],"to":[11,160],"its":[12,56],"sample":[13],"efficiency":[14],"on":[15,165,229],"expensive":[16],"black-box":[17],"functions.":[18],"While":[19],"most":[20],"BO":[21,69,98],"pipelines":[22],"begin":[23],"with":[24,32,70,155],"uniform":[25],"random":[26,153],"initialization,":[27],"default":[28,197],"values":[30],"shipped":[31],"popular":[33],"ML":[34],"libraries":[35],"such":[36],"as":[37,46,184,213],"scikit-learn":[38],"encode":[39,199],"implicit":[40],"expert":[41],"knowledge":[42],"and":[43,81,113,119,128,132,220],"could":[44],"serve":[45],"informative":[47],"starting":[48],"points":[49,71],"that":[50,196,208],"accelerate":[51],"convergence.":[52],"This":[53],"hypothesis,":[54],"despite":[55],"intuitive":[57],"appeal,":[58],"has":[59],"remained":[60],"largely":[61],"unexamined.":[62],"We":[63,90,205],"formalize":[64],"the":[65,83,166,175],"idea":[66],"by":[67],"initializing":[68],"drawn":[72],"from":[73,158],"truncated":[74],"Gaussian":[75],"distributions":[76],"centered":[77],"at":[78],"library":[79,230],"defaults":[80,176],"compare":[82],"resulting":[84],"trajectories":[85],"against":[86],"uniform-random":[88],"baseline.":[89],"conduct":[91],"an":[92,214],"extensive":[93],"empirical":[94],"evaluation":[95],"spanning":[96],"three":[97,103],"back-ends":[99],"(BoTorch,":[100],"Optuna,":[101],"Scikit-Optimize),":[102],"model":[104,218],"families":[105],"(Random":[106],"Forests,":[107],"Support":[108],"Vector":[109],"Machines,":[110],"Multilayer":[111],"Perceptrons),":[112],"five":[114],"benchmark":[115],"datasets":[116],"covering":[117],"classification":[118],"regression":[120],"tasks.":[121],"Performance":[122],"assessed":[124],"through":[125],"convergence":[126],"speed":[127],"final":[129,188],"predictive":[130],"quality,":[131],"statistical":[133],"significance":[134],"determined":[136],"via":[137],"one-sided":[138],"binomial":[139],"tests.":[140],"Across":[141],"all":[142],"conditions,":[143],"default-informed":[144],"initialization":[145],"yields":[146],"no":[147,194],"statistically":[148],"significant":[149],"advantage":[150],"over":[151,226],"purely":[152],"sampling,":[154],"p-values":[156],"ranging":[157],"0.141":[159],"0.908.":[161],"A":[162],"sensitivity":[163],"analysis":[164],"prior":[167],"variance":[168],"confirms":[169],"that,":[170],"while":[171],"tighter":[172],"concentration":[173],"around":[174],"improves":[177],"early":[178],"evaluations,":[179],"this":[180],"transient":[181],"benefit":[182],"vanishes":[183],"optimization":[185],"progresses,":[186],"leaving":[187],"performance":[189],"unchanged.":[190],"Our":[191],"results":[192],"provide":[193],"evidence":[195],"hyperparameters":[198],"useful":[200],"directional":[201],"information":[202],"optimization.":[204],"therefore":[206],"recommend":[207],"practitioners":[209],"treat":[210],"integral":[215],"part":[216],"of":[217],"development":[219],"favor":[221],"principled,":[222],"data-driven":[223],"search":[224],"strategies":[225],"heuristic":[227],"reliance":[228],"defaults.":[231]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-11T00:00:00"}