{"id":"https://openalex.org/W4385562474","doi":"https://doi.org/10.1145/3580305.3599420","title":"Machine Unlearning in Gradient Boosting Decision Trees","display_name":"Machine Unlearning in Gradient Boosting Decision Trees","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385562474","doi":"https://doi.org/10.1145/3580305.3599420"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599420","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599420","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th 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/A5047300176","display_name":"Huawei Lin","orcid":"https://orcid.org/0000-0002-2965-3158"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Huawei Lin","raw_affiliation_strings":["Rochester Institute of Technology, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology, Rochester, NY, USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067307769","display_name":"Jun Woo Chung","orcid":"https://orcid.org/0009-0004-3414-6283"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Woo Chung","raw_affiliation_strings":["Rochester Institute of Technology, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology, Rochester, NY, USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071172709","display_name":"Yingjie Lao","orcid":"https://orcid.org/0000-0002-9413-2455"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingjie Lao","raw_affiliation_strings":["Clemson University, Clemson, SC, USA"],"affiliations":[{"raw_affiliation_string":"Clemson University, Clemson, SC, USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075541712","display_name":"Weijie Zhao","orcid":"https://orcid.org/0000-0003-0967-1436"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weijie Zhao","raw_affiliation_strings":["Rochester Institute of Technology, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology, Rochester, NY, USA","institution_ids":["https://openalex.org/I155173764"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047300176"],"corresponding_institution_ids":["https://openalex.org/I155173764"],"apc_list":null,"apc_paid":null,"fwci":2.2468,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.90300574,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1374","last_page":"1383"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9901000261306763,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8599880933761597},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7199689149856567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6896059513092041},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5848293304443359},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.5658411979675293},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5038856863975525},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.48033347725868225},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4706798195838928},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10994201898574829}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8599880933761597},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7199689149856567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6896059513092041},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5848293304443359},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.5658411979675293},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5038856863975525},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.48033347725868225},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4706798195838928},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10994201898574829},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599420","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599420","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G4872545819","display_name":null,"funder_award_id":"2247619","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1488996941","https://openalex.org/W1678356000","https://openalex.org/W2024046085","https://openalex.org/W2051267297","https://openalex.org/W2120391124","https://openalex.org/W2156415837","https://openalex.org/W2535690855","https://openalex.org/W2560674852","https://openalex.org/W2745244103","https://openalex.org/W2795435272","https://openalex.org/W2946930197","https://openalex.org/W2961073278","https://openalex.org/W2978329087","https://openalex.org/W2987201163","https://openalex.org/W2997721570","https://openalex.org/W3026226589","https://openalex.org/W3035644192","https://openalex.org/W3081125651","https://openalex.org/W3130821513","https://openalex.org/W3135378441","https://openalex.org/W3138815606","https://openalex.org/W3169366506","https://openalex.org/W3172075261","https://openalex.org/W3174532363","https://openalex.org/W3175430527","https://openalex.org/W3176739818","https://openalex.org/W3194254965","https://openalex.org/W3202838631","https://openalex.org/W4212774754","https://openalex.org/W4290876045","https://openalex.org/W4296562799","https://openalex.org/W4312595359","https://openalex.org/W6796843635","https://openalex.org/W6797062389","https://openalex.org/W6839949769","https://openalex.org/W6840897724"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2393964553","https://openalex.org/W2121565117","https://openalex.org/W1487831638","https://openalex.org/W3108206494","https://openalex.org/W2739726746","https://openalex.org/W4242380336","https://openalex.org/W2141272333"],"abstract_inverted_index":{"Various":[0],"machine":[1,31,74,107,128],"learning":[2,42,75],"applications":[3],"take":[4],"users'":[5,18],"data":[6,19,52,149,198,206],"to":[7,16,24,45,48,56,58,115,122,172,181,201,222],"train":[8],"the":[9,28,33,38,50,60,81,95,102,117,127,152,173,177,184,192,196,209,227,260],"models.":[10],"Recently":[11],"enforced":[12],"legislation":[13],"requires":[14,187],"companies":[15],"remove":[17],"upon":[20],"requests,":[21],"i.e.,the":[22],"right":[23],"be":[25,46,223],"forgotten.":[26],"In":[27],"context":[29],"of":[30,97,120,148,160,176,262],"learning,":[32],"trained":[34,185],"model":[35,71,153,186,245],"potentially":[36],"memorizes":[37],"training":[39,174,210,229],"data.":[40],"Machine":[41],"algorithms":[43],"have":[44],"able":[47],"unlearn":[49],"user":[51],"that":[53,105,140,183,212],"are":[54],"requested":[55],"delete":[57],"meet":[59],"requirement.":[61],"Gradient":[62],"Boosting":[63],"Decision":[64],"Trees":[65],"(GBDT)":[66],"is":[67,101,112,220,236],"a":[68,88,145,158,218,232,239],"widely":[69],"deployed":[70],"in":[72],"many":[73],"applications.":[76],"However,":[77],"few":[78,188],"studies":[79],"investigate":[80,195],"unlearning":[82,90,108,118,129,138,242],"on":[83,109,253],"GBDT.":[84,93,110],"This":[85],"paper":[86],"proposes":[87],"novel":[89],"framework":[91,139],"for":[92],"To":[94],"best":[96],"our":[98,250,263],"knowledge,":[99],"this":[100],"first":[103],"work":[104],"considers":[106],"It":[111],"not":[113],"straightforward":[114],"transfer":[116],"methods":[119,252],"DNN":[121],"GBDT":[123],"settings.":[124],"We":[125,135,156,194,247],"formalized":[126],"problem":[130],"and":[131,142,167,199,244],"its":[132],"relaxed":[133],"version.":[134],"propose":[136],"an":[137,204],"efficiently":[141],"effectively":[143],"unlearns":[144],"given":[146],"collection":[147,159],"without":[150,225],"retraining":[151],"from":[154],"scratch.":[155],"introduce":[157],"techniques,":[161],"including":[162],"random":[163,168],"split":[164],"point":[165],"selection":[166],"partitioning":[169],"layers":[170],"training,":[171],"process":[175],"original":[178,228],"tree":[179],"models":[180],"ensure":[182],"subtree":[189,219],"retrainings":[190],"during":[191,208],"unlearning.":[193],"intermediate":[197],"statistics":[200],"store":[202],"as":[203,238],"auxiliary":[205],"structure":[207],"so":[211],"we":[213],"can":[214],"immediately":[215],"determine":[216],"if":[217],"required":[221],"retrained":[224],"touching":[226],"dataset.":[230],"Furthermore,":[231],"lazy":[233],"update":[234],"technique":[235],"proposed":[237,251],"trade-off":[240],"between":[241],"time":[243],"functionality.":[246],"experimentally":[248],"evaluate":[249],"public":[254],"datasets.":[255],"The":[256],"empirical":[257],"results":[258],"confirm":[259],"effectiveness":[261],"framework.":[264]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
