{"id":"https://openalex.org/W2963771792","doi":"https://doi.org/10.25080/majora-7ddc1dd1-011","title":"Better and faster hyperparameter optimization with Dask","display_name":"Better and faster hyperparameter optimization with Dask","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2963771792","doi":"https://doi.org/10.25080/majora-7ddc1dd1-011","mag":"2963771792"},"language":"en","primary_location":{"id":"doi:10.25080/majora-7ddc1dd1-011","is_oa":true,"landing_page_url":"https://doi.org/10.25080/majora-7ddc1dd1-011","pdf_url":"http://conference.scipy.org/proceedings/scipy2019/pdfs/scott_sievert.pdf","source":{"id":"https://openalex.org/S4220651651","display_name":"Proceedings of the Python in Science Conferences","issn_l":"2575-9752","issn":["2575-9752"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Python in Science Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"http://conference.scipy.org/proceedings/scipy2019/pdfs/scott_sievert.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057482475","display_name":"Scott Sievert","orcid":"https://orcid.org/0000-0002-4275-3452"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Scott Sievert","raw_affiliation_strings":["University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054881792","display_name":"Tom Augspurger","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tom Augspurger","raw_affiliation_strings":["University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018780923","display_name":"Matthew Rocklin","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Rocklin","raw_affiliation_strings":["University of Wisconsin-Madison"],"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Madison","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057482475"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":1.4002,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.86284585,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"125"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9993000030517578,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.996399998664856,"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":0.9768692255020142},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.7920082211494446},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7850017547607422},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6970529556274414},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.6153668761253357},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6099181175231934},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5321423411369324},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.44714978337287903},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4345901906490326},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.18198725581169128},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11360397934913635},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.08122986555099487}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9768692255020142},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7920082211494446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7850017547607422},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6970529556274414},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.6153668761253357},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6099181175231934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5321423411369324},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.44714978337287903},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4345901906490326},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.18198725581169128},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11360397934913635},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.08122986555099487},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.25080/majora-7ddc1dd1-011","is_oa":true,"landing_page_url":"https://doi.org/10.25080/majora-7ddc1dd1-011","pdf_url":"http://conference.scipy.org/proceedings/scipy2019/pdfs/scott_sievert.pdf","source":{"id":"https://openalex.org/S4220651651","display_name":"Proceedings of the Python in Science Conferences","issn_l":"2575-9752","issn":["2575-9752"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Python in Science Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.25080/majora-7ddc1dd1-011","is_oa":true,"landing_page_url":"https://doi.org/10.25080/majora-7ddc1dd1-011","pdf_url":"http://conference.scipy.org/proceedings/scipy2019/pdfs/scott_sievert.pdf","source":{"id":"https://openalex.org/S4220651651","display_name":"Proceedings of the Python in Science Conferences","issn_l":"2575-9752","issn":["2575-9752"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Python in Science Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963771792.pdf","grobid_xml":"https://content.openalex.org/works/W2963771792.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W114517082","https://openalex.org/W1506859583","https://openalex.org/W1522301498","https://openalex.org/W1638440761","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W2016043834","https://openalex.org/W2053834050","https://openalex.org/W2097998348","https://openalex.org/W2101234009","https://openalex.org/W2106411961","https://openalex.org/W2124541940","https://openalex.org/W2131241448","https://openalex.org/W2135046866","https://openalex.org/W2176412452","https://openalex.org/W2187089797","https://openalex.org/W2542768043","https://openalex.org/W2556522401","https://openalex.org/W2619516334","https://openalex.org/W2771005692","https://openalex.org/W2785873347","https://openalex.org/W2804268694","https://openalex.org/W2899771611","https://openalex.org/W2963285578","https://openalex.org/W2963326510","https://openalex.org/W2963423218","https://openalex.org/W2963474950","https://openalex.org/W2963815651","https://openalex.org/W3003196782","https://openalex.org/W3141595720","https://openalex.org/W4230471307","https://openalex.org/W4234117503","https://openalex.org/W4289763996","https://openalex.org/W4381304672"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4205712847","https://openalex.org/W4281646320","https://openalex.org/W3169687406","https://openalex.org/W2954882791","https://openalex.org/W4388119537","https://openalex.org/W4287818966","https://openalex.org/W1974336862","https://openalex.org/W3014750173","https://openalex.org/W3192751261"],"abstract_inverted_index":{"Nearly":[0],"every":[1],"machine":[2],"learning":[3,116],"model":[4,18],"requires":[5],"hyperparameters,":[6],"parameters":[7],"that":[8,77],"the":[9,21,70,92,104],"user":[10],"must":[11],"specify":[12],"before":[13],"training":[14,45],"begins":[15],"and":[16,68,83,99],"influence":[17],"performance.":[19],"Finding":[20],"optimal":[22],"set":[23],"of":[24,107],"hyperparameters":[25,42,113],"is":[26],"often":[27],"a":[28,47,74],"time-and":[29],"resource-consuming":[30],"process.":[31],"A":[32],"recent":[33],"breakthrough":[34],"hyperparameter":[35,54],"optimization":[36],"algorithm,":[37],"Hyperband":[38,67,93,108],"finds":[39,110],"high":[40,111],"performing":[41,112],"with":[43],"minimal":[44],"via":[46],"principled":[48],"early":[49],"stopping":[50],"scheme":[51],"for":[52,114],"random":[53],"selection":[55],"[LJD":[56],"+":[57],"18].":[58],"This":[59],"paper":[60],"will":[61],"provide":[62],"an":[63],"intuitive":[64],"introduction":[65],"to":[66,80,91,95],"explain":[69],"implementation":[71,88,106],"in":[72],"Dask,":[73],"Python":[75,79],"library":[76],"scales":[78],"larger":[81],"datasets":[82],"more":[84],"computational":[85],"resources.":[86],"The":[87],"makes":[89],"adjustments":[90],"algorithm":[94],"exploit":[96],"Dask's":[97],"capabilities":[98],"parallel":[100],"processing.":[101],"In":[102],"experiments,":[103],"Dask":[105],"rapidly":[109],"deep":[115],"models.":[117]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
