{"id":"https://openalex.org/W3195699808","doi":"https://doi.org/10.25528/016","title":"Bayesian Hyperparameter Optimization - Relational and Scalable Surrogate Models for Hyperparameter Optimization Across Problem Instances","display_name":"Bayesian Hyperparameter Optimization - Relational and Scalable Surrogate Models for Hyperparameter Optimization Across Problem Instances","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W3195699808","doi":"https://doi.org/10.25528/016","mag":"3195699808"},"language":"en","primary_location":{"id":"doi:10.25528/016","is_oa":true,"landing_page_url":"https://doi.org/10.25528/016","pdf_url":null,"source":{"id":"https://openalex.org/S4306400083","display_name":"HilDok \u2013 Institutional Repository (Universit\u00e4t Hildesheim)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210142515","host_organization_name":"Springer Nature (Switzerland)","host_organization_lineage":["https://openalex.org/I4210142515"],"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-journal"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.25528/016","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010971228","display_name":"Nicolas Schilling","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Schilling, Nicolas","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5010971228"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29277077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9803000092506409,"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":0.9803000092506409,"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":0.9386000037193298,"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":0.8717820644378662},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.7743710279464722},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7588306665420532},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.7532414197921753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6847681999206543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6680747866630554},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5208778381347656},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5016539096832275},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.47251376509666443},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.42646342515945435},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.41709965467453003},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24035292863845825},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.21115970611572266},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.14300256967544556}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8717820644378662},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.7743710279464722},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7588306665420532},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.7532414197921753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6847681999206543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6680747866630554},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5208778381347656},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5016539096832275},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.47251376509666443},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.42646342515945435},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.41709965467453003},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24035292863845825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.21115970611572266},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.14300256967544556},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.25528/016","is_oa":true,"landing_page_url":"https://doi.org/10.25528/016","pdf_url":null,"source":{"id":"https://openalex.org/S4306400083","display_name":"HilDok \u2013 Institutional Repository (Universit\u00e4t Hildesheim)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210142515","host_organization_name":"Springer Nature (Switzerland)","host_organization_lineage":["https://openalex.org/I4210142515"],"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-journal"},{"id":"mag:3195699808","is_oa":false,"landing_page_url":"https://hildok.bsz-bw.de/files/972/schilling-thesis.pdf","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:10.25528/016","is_oa":true,"landing_page_url":"https://doi.org/10.25528/016","pdf_url":null,"source":{"id":"https://openalex.org/S4306400083","display_name":"HilDok \u2013 Institutional Repository (Universit\u00e4t Hildesheim)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210142515","host_organization_name":"Springer Nature (Switzerland)","host_organization_lineage":["https://openalex.org/I4210142515"],"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-journal"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3180812695","https://openalex.org/W1663203009","https://openalex.org/W2915909819","https://openalex.org/W3112667379","https://openalex.org/W2970941210","https://openalex.org/W3124569606","https://openalex.org/W2561476038","https://openalex.org/W3082696595","https://openalex.org/W3200686130","https://openalex.org/W2964241627","https://openalex.org/W3210457622","https://openalex.org/W3023570597","https://openalex.org/W3014266848","https://openalex.org/W2913573237","https://openalex.org/W3132585319","https://openalex.org/W3021678865","https://openalex.org/W2955449169","https://openalex.org/W3102244861","https://openalex.org/W2991037246","https://openalex.org/W1520137306"],"abstract_inverted_index":{"Machine":[0],"learning":[1,9,33,81,111,195,387,395,401],"is":[2,35,52,63,135,266,363],"often":[3],"confronted":[4],"with":[5,248],"the":[6,25,40,46,49,74,91,112,120,129,172,188,204,212,226,249,269,277,289,331,334,380,384,417],"problem":[7,26,61,75,92,213,227,432],"of":[8,15,24,48,76,93,102,131,214,228,271,291,333,336,349,358,386],"prediction":[10,178],"models":[11,31,167,298],"on":[12,118],"a":[13,136,147,158,177,232,256,262,328,351],"set":[14,23,241],"observed":[16],"data":[17,22,199,240,337,398],"points.":[18],"Given":[19],"an":[20,59,154,356],"expressive":[21],"to":[27,85,110,170,174,238,279,300,340,366,369,438],"solve,":[28],"using":[29,355],"powerful":[30],"and":[32,82,313],"algorithms":[34],"only":[36,64,152],"hindered":[37],"by":[38,66,194,203,268,354,382,416],"setting":[39],"right":[41,57,121],"configurations":[42,58,104,251],"for":[43,79,138,146,179,192,343,434],"both.":[44],"Unfortunately,":[45,322],"magnitude":[47],"performance":[50,403,425],"difference":[51],"large,":[53],"which":[54,134,362],"makes":[55],"choosing":[56],"additional":[60],"that":[62,105,126,186,265,409,423],"solved":[65,202],"experienced":[67],"practitioners.\r\nIn":[68],"this":[69],"thesis,":[70],"we":[71,115,224,236,407,421],"will":[72],"address":[73],"hyperparameter":[77,103,215,229,250,311,319,402,424],"optimization":[78,164,216,230,320],"machine":[80,95],"present":[83],"ways":[84],"solve":[86],"it.":[87],"We":[88,97,260,303,345],"firstly":[89],"introduce":[90,116],"supervised":[94],"learning.":[96],"then":[98,175],"discuss":[99],"many":[100],"examples":[101],"can":[106,151,413,426],"be":[107,301,341,414,428],"considered":[108],"prior":[109],"model.":[113],"Afterwards,":[114],"methods":[117,125,348],"finding":[119],"configurations,":[122],"especially":[123],"those":[124],"work":[127,314],"in":[128,162,217,255,284,317,330,390],"scheme":[130],"Bayesian":[132],"optimization,":[133],"framework":[137],"optimizing":[139],"black-box":[140,163],"functions.":[141],"Black-boxes":[142],"are":[143,168,310,324],"functions":[144],"where":[145,235],"given":[148],"input":[149],"one":[150],"observe":[153],"output":[155],"after":[156],"running":[157],"costly":[159],"procedure.":[160],"Usually,":[161],"so-called":[165],"surrogate":[166,263,297],"learned":[169],"reconstruct":[171],"observations":[173],"offer":[176],"unobserved":[180],"configurations.":[181],"Fortunately,":[182],"recent":[183],"outcomes":[184],"show":[185,346,408,422],"transfering":[187],"knowledge":[189,293],"across":[190,197,388,396,404,430],"problems,":[191],"example":[193,435],"surrogates":[196],"different":[198,220,397,405,431],"sets":[200],"being":[201],"same":[205],"model":[206,264,411],"class,":[207],"shows":[208],"promising":[209],"results.\r\nWe":[210],"tackle":[211],"mainly":[218],"two":[219],"ways.":[221],"At":[222],"first,":[223],"consider":[225,304],"as":[231,243,245,252,274,276,283,288,307,327],"recommendation":[233],"problem,":[234],"want":[237],"learn":[239,280,367],"features":[242,254],"well":[244,275,316],"their":[246],"interaction":[247],"latent":[253,281],"factorization":[257,285],"based":[258],"approach.":[259],"build":[261],"inspired":[267],"complexity":[270],"neural":[272],"networks":[273],"ability":[278],"embeddings":[282],"machines.":[286],"Secondly,":[287],"amount":[290],"meta":[292],"increases":[294],"every":[295],"day,":[296],"need":[299],"scalable.":[302],"Gaussian":[305,352,359],"processes,":[306],"they":[308,323],"themselves":[309],"free":[312],"very":[315,376],"most":[318],"cases.":[321],"not":[325],"scalable,":[326],"matrix":[329],"size":[332],"number":[335],"points":[338],"has":[339],"inverted":[342],"inference.":[344],"various":[347],"simplifying":[350],"process":[353,360],"ensemble":[357],"experts,":[361],"much":[364],"faster":[365],"due":[368],"its":[370],"paralellization":[371],"properties":[372],"while":[373],"still":[374],"showing":[375],"competitive":[377],"performance.\r\nWe":[378],"conclude":[379],"thesis":[381],"discussing":[383],"aspect":[385],"problems":[389],"more":[391],"detail":[392],"than":[393],"simply":[394],"sets.":[399],"By":[400],"models,":[406],"also":[410],"choice":[412],"handled":[415],"proposed":[418],"algorithms.":[419],"Additionally,":[420],"even":[427],"transfered":[429],"tasks,":[433],"from":[436],"classification":[437],"regression.":[439]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}