{"id":"https://openalex.org/W2772678505","doi":"https://doi.org/10.1145/3135974.3135994","title":"HyperDrive","display_name":"HyperDrive","publication_year":2017,"publication_date":"2017-11-30","ids":{"openalex":"https://openalex.org/W2772678505","doi":"https://doi.org/10.1145/3135974.3135994","mag":"2772678505"},"language":"en","primary_location":{"id":"doi:10.1145/3135974.3135994","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3135974.3135994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037534081","display_name":"Jeff Rasley","orcid":null},"institutions":[{"id":"https://openalex.org/I175594653","display_name":"John Brown University","ror":"https://ror.org/02ct41q97","country_code":"US","type":"education","lineage":["https://openalex.org/I175594653"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeff Rasley","raw_affiliation_strings":["Brown University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brown University","institution_ids":["https://openalex.org/I175594653"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040302174","display_name":"Yuxiong He","orcid":"https://orcid.org/0000-0003-0478-8854"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yuxiong He","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381152","display_name":"Feng Yan","orcid":"https://orcid.org/0000-0001-9840-7754"},"institutions":[{"id":"https://openalex.org/I134113660","display_name":"University of Nevada, Reno","ror":"https://ror.org/01keh0577","country_code":"US","type":"education","lineage":["https://openalex.org/I134113660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Yan","raw_affiliation_strings":["University of Nevada, Reno"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Nevada, Reno","institution_ids":["https://openalex.org/I134113660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022644245","display_name":"Olatunji Ruwase","orcid":"https://orcid.org/0000-0002-5508-0728"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Olatunji Ruwase","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036529548","display_name":"Rodrigo Fonseca","orcid":"https://orcid.org/0000-0001-9662-2661"},"institutions":[{"id":"https://openalex.org/I175594653","display_name":"John Brown University","ror":"https://ror.org/02ct41q97","country_code":"US","type":"education","lineage":["https://openalex.org/I175594653"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rodrigo Fonseca","raw_affiliation_strings":["Brown University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brown University","institution_ids":["https://openalex.org/I175594653"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9242,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.95058843,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"13"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9952999949455261,"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":0.9390329122543335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.832133412361145},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.7761118412017822},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6959823369979858},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6552616953849792},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6214789748191833},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6038693189620972},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5938940644264221},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.49514809250831604},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.18070822954177856},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.10646799206733704},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.09359678626060486}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9390329122543335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.832133412361145},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.7761118412017822},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6959823369979858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6552616953849792},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6214789748191833},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6038693189620972},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5938940644264221},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.49514809250831604},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.18070822954177856},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.10646799206733704},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09359678626060486},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3135974.3135994","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3135974.3135994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6499999761581421,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W1442374986","https://openalex.org/W1569788011","https://openalex.org/W1591801644","https://openalex.org/W2074702228","https://openalex.org/W2102539288","https://openalex.org/W2112364454","https://openalex.org/W2113207845","https://openalex.org/W2138019504","https://openalex.org/W2147967768","https://openalex.org/W2152175008","https://openalex.org/W2155893237","https://openalex.org/W2181607856","https://openalex.org/W2266822037","https://openalex.org/W2551396370","https://openalex.org/W2577837170","https://openalex.org/W2751836095","https://openalex.org/W2752331852","https://openalex.org/W2950182411","https://openalex.org/W2951781666","https://openalex.org/W2963000224","https://openalex.org/W3008801451","https://openalex.org/W3118608800","https://openalex.org/W4212774754"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4281646320","https://openalex.org/W4205712847","https://openalex.org/W3169687406","https://openalex.org/W1974336862","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3114025147","https://openalex.org/W4287818966","https://openalex.org/W3192751261"],"abstract_inverted_index":{"The":[0,148],"quality":[1,105,180],"of":[2,33,65,70,89,128],"machine":[3],"learning":[4,8,136],"(ML)":[5],"and":[6,47,68,72,86,99,106,120,135,141,145,168],"deep":[7,146],"(DL)":[9],"models":[10,43],"are":[11,20],"very":[12],"sensitive":[13],"to":[14,36,40,56,102,117,159,170],"many":[15],"different":[16,132],"adjustable":[17],"parameters":[18],"that":[19,80,151],"set":[21],"before":[22],"training":[23,62,155],"even":[24],"begins,":[25],"commonly":[26],"called":[27],"hyperparameters.":[28,90],"Efficient":[29],"hyperparameter":[30,113],"exploration":[31,114],"is":[32,50],"great":[34],"importance":[35],"practitioners":[37],"in":[38],"order":[39],"find":[41],"high-quality":[42],"with":[44,96,162,173,182],"affordable":[45],"time":[46,71],"cost.":[48,107],"This":[49],"however":[51],"a":[52,57,76,111,125],"challenging":[53],"process":[54,156],"due":[55],"huge":[58],"search":[59,167],"space,":[60],"expensive":[61],"runtime,":[63],"sparsity":[64],"good":[66],"configurations,":[67],"scarcity":[69],"resources.":[73],"We":[74,108,138],"develop":[75],"scheduling":[77,98,122],"algorithm":[78],"POP":[79,140],"quickly":[81],"identifies":[82],"among":[83],"promising,":[84],"opportunistic":[85],"poor":[87],"configurations":[88],"It":[91],"infuses":[92],"probabilistic":[93],"model-based":[94],"classification":[95],"dynamic":[97],"early":[100],"termination":[101],"jointly":[103],"optimize":[104],"also":[109],"build":[110],"comprehensive":[112],"infrastructure,":[115],"HyperDrive,":[116],"support":[118],"existing":[119],"future":[121],"algorithms":[123],"for":[124],"wide":[126],"range":[127],"usage":[129],"scenarios":[130],"across":[131],"ML/DL":[133],"frameworks":[134],"domains.":[137],"evaluate":[139],"HyperDrive":[142],"using":[143],"complex":[144],"models.":[147],"results":[149],"show":[150],"we":[152],"speedup":[153],"the":[154],"by":[157],"up":[158,169],"6.7x":[160],"compared":[161,172,181],"basic":[163],"approaches":[164,175],"like":[165],"random/grid":[166],"2.1x":[171],"state-of-the-art":[174],"while":[176],"achieving":[177],"similar":[178],"model":[179],"prior":[183],"work.":[184]},"counts_by_year":[{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2017-12-22T00:00:00"}