{"id":"https://openalex.org/W4403370486","doi":"https://doi.org/10.48550/arxiv.2408.02533","title":"LMEMs for post-hoc analysis of HPO Benchmarking","display_name":"LMEMs for post-hoc analysis of HPO Benchmarking","publication_year":2024,"publication_date":"2024-08-05","ids":{"openalex":"https://openalex.org/W4403370486","doi":"https://doi.org/10.48550/arxiv.2408.02533"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2408.02533","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.02533","pdf_url":"https://arxiv.org/pdf/2408.02533","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/2408.02533","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111397960","display_name":"Anton Geburek","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Geburek, Anton","raw_affiliation_strings":["Machine Learning Lab , University of Freiburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning Lab , University of Freiburg","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058000344","display_name":"Neeratyoy Mallik","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mallik, Neeratyoy","raw_affiliation_strings":["Machine Learning Lab , University of Freiburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning Lab , University of Freiburg","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039793317","display_name":"Danny Stoll","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stoll, Danny","raw_affiliation_strings":["Machine Learning Lab , University of Freiburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning Lab , University of Freiburg","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016899919","display_name":"Xavier Bouthillier","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210164802","display_name":"Mila - Quebec Artificial Intelligence Institute","ror":"https://ror.org/05c22rx21","country_code":"CA","type":"facility","lineage":["https://openalex.org/I4210164802"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Bouthillier, Xavier","raw_affiliation_strings":["Mila , Montreal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mila , Montreal","institution_ids":["https://openalex.org/I4210164802"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031002895","display_name":"Frank Hutter","orcid":"https://orcid.org/0000-0002-2037-3694","observed_orcids":["https://orcid.org/0000-0002-2037-3694"]},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]},{"id":"https://openalex.org/I4405263145","display_name":"ELLIS Institute T\u00fcbingen","ror":"https://ror.org/036z3g533","country_code":"DE","type":"other","lineage":["https://openalex.org/I4405263145"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hutter, Frank","raw_affiliation_strings":["ELLIS Institute T\u00fcbingen","Machine Learning Lab , University of Freiburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ELLIS Institute T\u00fcbingen","institution_ids":["https://openalex.org/I4405263145"]},{"raw_affiliation_string":"Machine Learning Lab , University of Freiburg","institution_ids":["https://openalex.org/I161046081"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9940999746322632,"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":0.9940999746322632,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.0007999999797903001,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"hyperparameter optimization","score":0.9819999933242798},{"id":"https://openalex.org/keywords/benchmarking","display_name":"benchmarking","score":0.781000018119812},{"id":"https://openalex.org/keywords/linear-mixed-models","display_name":"linear mixed models","score":0.8610000014305115},{"id":"https://openalex.org/keywords/post-hoc-analysis","display_name":"post hoc analysis","score":0.9459999799728394},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"pairwise comparison","score":0.6430000066757202},{"id":"https://openalex.org/keywords/significance-testing","display_name":"significance testing","score":0.8389999866485596}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7988463640213013},{"id":"https://openalex.org/C67761136","wikidata":"https://www.wikidata.org/wiki/Q2105849","display_name":"Post-hoc analysis","level":2,"score":0.7492929100990295},{"id":"https://openalex.org/C2992886853","wikidata":"https://www.wikidata.org/wiki/Q18381816","display_name":"Post hoc","level":2,"score":0.7070508003234863},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41392242908477783},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2509857416152954},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20488902926445007},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1055443286895752},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0915793776512146},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.049188196659088135},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.040050774812698364}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2408.02533","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.02533","pdf_url":"https://arxiv.org/pdf/2408.02533","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2408.02533","doi":"https://doi.org/10.48550/arxiv.2408.02533","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2408.02533","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2408.02533","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.02533","pdf_url":"https://arxiv.org/pdf/2408.02533","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1741598991","display_name":null,"funder_award_id":"INST 39/963-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G2805343336","display_name":null,"funder_award_id":"\u201cINST 39/963-1 FUGG\u201d","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G434337789","display_name":null,"funder_award_id":"HORIZON2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4855614693","display_name":null,"funder_award_id":"101045765","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G5212067111","display_name":"Deep Learning Cluster","funder_award_id":"417962828","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G7291426837","display_name":null,"funder_award_id":"39/963-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8079567275","display_name":"Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization","funder_award_id":"952215","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403370486.pdf","grobid_xml":"https://content.openalex.org/works/W4403370486.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W6959213303","https://openalex.org/W4389500191","https://openalex.org/W3168606547","https://openalex.org/W7160958230","https://openalex.org/W3081580854","https://openalex.org/W4404313875","https://openalex.org/W3128997905","https://openalex.org/W7165659912","https://openalex.org/W3200635595","https://openalex.org/W3179950556"],"abstract_inverted_index":{"The":[0],"importance":[1],"of":[2,72],"tuning":[3],"hyperparameters":[4],"in":[5,24,118],"Machine":[6],"Learning":[7,11],"(ML)":[8],"and":[9,18,30,79],"Deep":[10],"(DL)":[12],"is":[13],"established":[14],"through":[15,103],"empirical":[16],"research":[17],"applications,":[19],"evident":[20],"from":[21],"the":[22,35,83,108,119],"increase":[23],"new":[25],"hyperparameter":[26],"optimization":[27],"(HPO)":[28],"algorithms":[29],"benchmarks":[31],"steadily":[32],"added":[33],"by":[34],"community.":[36],"However,":[37],"current":[38,97],"benchmarking":[39,74],"practices":[40],"using":[41],"averaged":[42],"performance":[43],"across":[44],"many":[45],"datasets":[46],"may":[47],"obscure":[48],"key":[49],"differences":[50],"between":[51],"HPO":[52,73],"methods,":[53],"especially":[54],"for":[55,69],"pairwise":[56],"comparisons.":[57],"In":[58],"this":[59,102],"work,":[60],"we":[61],"apply":[62],"Linear":[63],"Mixed-Effect":[64],"Models-based":[65],"(LMEMs)":[66],"significance":[67],"testing":[68],"post-hoc":[70],"analysis":[71,98],"runs.":[75],"LMEMs":[76],"allow":[77],"flexible":[78],"expressive":[80],"modeling":[81],"on":[82,107],"entire":[84],"experiment":[85,111],"data,":[86],"including":[87],"information":[88],"such":[89],"as":[90],"benchmark":[91],"meta-features,":[92],"offering":[93],"deeper":[94],"insights":[95,115],"than":[96],"practices.":[99],"We":[100],"demonstrate":[101],"a":[104],"case":[105],"study":[106],"PriorBand":[109],"paper's":[110],"data":[112],"to":[113],"find":[114],"not":[116],"reported":[117],"original":[120],"work.":[121]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2024-10-14T00:00:00"}