{"id":"https://openalex.org/W4387848555","doi":"https://doi.org/10.1145/3583780.3615996","title":"RT2S: A Framework for Learning with Noisy Labels","display_name":"RT2S: A Framework for Learning with Noisy Labels","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387848555","doi":"https://doi.org/10.1145/3583780.3615996"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615996","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","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/A5101620097","display_name":"Indranil Bhattacharya","orcid":"https://orcid.org/0009-0006-5828-8627"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Indranil Bhattacharya","raw_affiliation_strings":["Amazon.com, Inc., Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Inc., Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049390918","display_name":"\u5247\u6075 \u53ea\u57dc","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ze Ye","raw_affiliation_strings":["Amazon.com, Inc., Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Inc., Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052904116","display_name":"Kaushik Pavani","orcid":"https://orcid.org/0009-0008-5536-7068"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaushik Pavani","raw_affiliation_strings":["Amazon.com, Inc., Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Inc., Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031212349","display_name":"Sunny Dasgupta","orcid":"https://orcid.org/0009-0001-8223-688X"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sunny Dasgupta","raw_affiliation_strings":["Amazon.com, Inc., Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Inc., Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101620097"],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":0.174,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57636939,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"5234","last_page":"5235"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9975000023841858,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9961000084877014,"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":0.784112811088562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6776829957962036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.640277624130249},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5622584223747253},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5576326251029968},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5416879653930664},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48864418268203735},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35965776443481445}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784112811088562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6776829957962036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.640277624130249},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5622584223747253},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5576326251029968},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5416879653930664},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48864418268203735},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35965776443481445},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615996","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1514928307","https://openalex.org/W2963697299","https://openalex.org/W3042609801","https://openalex.org/W3156669901"],"related_works":["https://openalex.org/W1185300216","https://openalex.org/W2961085424","https://openalex.org/W2954163146","https://openalex.org/W4224009465","https://openalex.org/W4306674287","https://openalex.org/W2899086345","https://openalex.org/W4286629047","https://openalex.org/W2896057011","https://openalex.org/W4205958290","https://openalex.org/W4384212932"],"abstract_inverted_index":{"We":[0,101],"introduce":[1],"Robust":[2],"Training":[3],"with":[4,15,93],"Trust":[5],"Scores":[6],"(RT2S),":[7],"a":[8,21,89,120,126],"framework":[9],"to":[10,71,96],"train":[11],"machine":[12,106],"learning":[13,107],"classifiers":[14,150],"potentially":[16],"noisy":[17],"labels.":[18],"RT2S":[19,103,134],"calculates":[20],"trust":[22,37,52],"score":[23],"for":[24],"each":[25],"training":[26,45,77,105],"sample,":[27],"which":[28],"indicates":[29],"the":[30,60,64,76,81,84,94],"quality":[31],"of":[32,83,144],"its":[33],"corresponding":[34],"label.":[35],"These":[36],"scores":[38,53,70],"are":[39,54],"employed":[40],"as":[41,125],"sample":[42],"weights":[43],"during":[44,48],"and":[46,79,138],"optionally":[47],"threshold":[49],"optimization.":[50],"The":[51],"generated":[55,118],"from":[56],"two":[57],"sources:":[58],"(i)":[59],"model's":[61],"confidence":[62],"in":[63,75,151],"observed":[65],"label,":[66,86],"leveraging":[67],"out-of-fold":[68],"prediction":[69],"detect":[72],"anomalous":[73],"labels":[74,117],"data,":[78],"(ii)":[80],"probability":[82],"correct":[85],"ascertained":[87],"by":[88,104,119],"Large":[90],"Language":[91],"Model":[92],"ability":[95],"identify":[97],"biased":[98],"label":[99],"noise.":[100],"evaluate":[102],"models":[108],"on":[109],"6":[110],"product":[111],"classification":[112,122],"datasets":[113],"that":[114,133],"utilize":[115],"low-quality":[116],"rule-based":[121,149],"engine":[123],"acting":[124],"surrogate":[127],"labeler.":[128],"Our":[129],"experimental":[130],"findings":[131],"indicate":[132],"outperforms":[135],"all":[136],"baselines,":[137],"achieves":[139],"an":[140],"average":[141],"accuracy":[142],"improvement":[143],"4.38%":[145],"(max":[146],"7.18%)":[147],"over":[148],"particular.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}