{"id":"https://openalex.org/W4290878290","doi":"https://doi.org/10.1145/3534678.3542630","title":"Gradual AutoML using Lale","display_name":"Gradual AutoML using Lale","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290878290","doi":"https://doi.org/10.1145/3534678.3542630"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3542630","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3542630","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5079080602","display_name":"Martin Hirzel","orcid":"https://orcid.org/0009-0006-8840-6065"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Martin Hirzel","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082592545","display_name":"Kiran Kate","orcid":"https://orcid.org/0009-0003-9688-9245"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kiran Kate","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089095944","display_name":"Parikshit Ram","orcid":"https://orcid.org/0000-0002-9456-029X"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Parikshit Ram","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079889502","display_name":"Avraham Shinnar","orcid":"https://orcid.org/0000-0001-6259-0016"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Avraham Shinnar","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077279308","display_name":"Jason Tsay","orcid":"https://orcid.org/0000-0002-8085-5708"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason Tsay","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5079080602"],"corresponding_institution_ids":["https://openalex.org/I1341412227"],"apc_list":null,"apc_paid":null,"fwci":0.6233,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67205344,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4794","last_page":"4795"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9247999787330627,"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":0.9247999787330627,"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/computer-science","display_name":"Computer science","score":0.684241533279419},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.6166885495185852},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.6013779640197754},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.4700174629688263},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44033482670783997},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.424642413854599},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41886985301971436},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4035411477088928},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38669469952583313},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.18146535754203796}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.684241533279419},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.6166885495185852},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.6013779640197754},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.4700174629688263},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44033482670783997},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.424642413854599},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41886985301971436},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4035411477088928},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38669469952583313},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.18146535754203796},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3542630","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3542630","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3126131865","https://openalex.org/W2044344400","https://openalex.org/W2083611981","https://openalex.org/W4253186488","https://openalex.org/W1996938127","https://openalex.org/W2043380045","https://openalex.org/W4231814374","https://openalex.org/W2099557065","https://openalex.org/W2484410460","https://openalex.org/W2250278543"],"abstract_inverted_index":{"Lale":[0,54,88,117,153],"is":[1,11,55],"a":[2,28,68,72],"sklearn-compatible":[3],"library":[4],"for":[5,18,118,133,158],"automated":[6],"machine":[7,22],"learning":[8,23],"(AutoML).":[9],"It":[10,129],"open-source":[12],"(https://github.com/ibm/lale)":[13],"and":[14,39,48,63,143,147,171],"addresses":[15],"the":[16,51,114],"need":[17],"gradual":[19,69],"automation":[20,52,80],"of":[21,74,86,99,116,168],"as":[24,137],"opposed":[25],"to":[26,37,81,107,124,176,194],"offering":[27,71],"black-box":[29],"AutoML":[30,33,132,191,197],"tool.":[31],"Black-box":[32],"tools":[34],"are":[35],"difficult":[36],"customize":[38],"thus":[40],"restrict":[41],"data":[42,94],"scientists":[43,95],"in":[44,50],"leveraging":[45],"their":[46,100,169],"knowledge":[47],"intuition":[49],"process.":[53],"built":[56],"on":[57],"three":[58],"principles:":[59],"progressive":[60],"disclosure,":[61],"orthogonality,":[62],"least":[64],"surprise.":[65],"These":[66],"enable":[67],"approach":[70],"spectrum":[73],"usage":[75],"patterns":[76],"starting":[77],"from":[78],"total":[79],"controlling":[82],"almost":[83],"every":[84],"aspect":[85],"AutoML.":[87],"provides":[89],"compositional":[90],"constructs":[91],"that":[92],"let":[93],"control":[96,189],"some":[97],"aspects":[98,105],"pipelines":[101],"while":[102],"leaving":[103],"other":[104],"free":[106],"be":[108,195],"searched":[109],"automatically.":[110],"This":[111],"tutorial":[112,173,181],"demonstrates":[113],"use":[115],"various":[119],"machine-learning":[120],"tasks,":[121],"showing":[122],"how":[123,175,184],"progressively":[125],"exercise":[126,187],"more":[127,166],"customization.":[128],"also":[130,164],"covers":[131,174],"advanced":[134],"scenarios":[135],"such":[136],"class":[138],"imbalance":[139],"correction,":[140],"bias":[141],"detection":[142],"mitigation,":[144],"multi-objective":[145],"optimization,":[146],"working":[148],"with":[149,155],"multi-table":[150],"datasets.":[151],"While":[152],"comes":[154],"hyperparameter":[156],"specifications":[157],"216":[159],"operators":[160,167],"out-of-the-box,":[161],"users":[162],"can":[163,186],"add":[165],"own,":[170],"this":[172,180],"do":[177],"that.":[178],"Overall,":[179],"teaches":[182],"you":[183,185],"fine-grained":[188],"over":[190],"without":[192],"having":[193],"an":[196],"expert.":[198]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}