{"id":"https://openalex.org/W3093903225","doi":"https://doi.org/10.1109/tnnls.2020.3027605","title":"Shattering Distribution for Active Learning","display_name":"Shattering Distribution for Active Learning","publication_year":2020,"publication_date":"2020-10-21","ids":{"openalex":"https://openalex.org/W3093903225","doi":"https://doi.org/10.1109/tnnls.2020.3027605","mag":"3093903225","pmid":"https://pubmed.ncbi.nlm.nih.gov/33085620"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2020.3027605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3027605","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaofeng Cao","orcid":"https://orcid.org/0000-0002-4191-009X"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiaofeng Cao","raw_affiliation_strings":["Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-4191-009X","affiliations":[{"raw_affiliation_string":"Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ivor W. Tsang","orcid":"https://orcid.org/0000-0003-2211-8176"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ivor W. Tsang","raw_affiliation_strings":["Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-2211-8176","affiliations":[{"raw_affiliation_string":"Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":0.7823,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.78876663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":"33","issue":"1","first_page":"215","last_page":"228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9940999746322632,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9940999746322632,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.0024999999441206455,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.00039999998989515007,"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/set","display_name":"Set (abstract data type)","score":0.5436999797821045},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.524399995803833},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.5231000185012817},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49709999561309814},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.47189998626708984},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4456000030040741},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.43140000104904175},{"id":"https://openalex.org/keywords/empirical-distribution-function","display_name":"Empirical distribution function","score":0.3991999924182892},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.37880000472068787}],"concepts":[{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.524399995803833},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5051000118255615},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49709999561309814},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4546000063419342},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4456000030040741},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.43140000104904175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4212999939918518},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4083000123500824},{"id":"https://openalex.org/C98385598","wikidata":"https://www.wikidata.org/wiki/Q1339385","display_name":"Empirical distribution function","level":2,"score":0.3991999924182892},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3950999975204468},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.36809998750686646},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.32679998874664307},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C2779915298","wikidata":"https://www.wikidata.org/wiki/Q7604400","display_name":"Statistical learning theory","level":3,"score":0.27059999108314514},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2572000026702881},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2551000118255615}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2020.3027605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3027605","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:33085620","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33085620","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1888224328","display_name":"Adversarial Learning of Hybrid Representation","funder_award_id":"DP200101328","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G3263795131","display_name":"Towards interpretable deep learning with limited examples","funder_award_id":"DP180100106","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1480930109","https://openalex.org/W1514707997","https://openalex.org/W1528361845","https://openalex.org/W1588635063","https://openalex.org/W1968200975","https://openalex.org/W2008989859","https://openalex.org/W2009134948","https://openalex.org/W2012878613","https://openalex.org/W2029538739","https://openalex.org/W2029604420","https://openalex.org/W2042932437","https://openalex.org/W2072782484","https://openalex.org/W2076541756","https://openalex.org/W2080021732","https://openalex.org/W2109751703","https://openalex.org/W2114188922","https://openalex.org/W2114232233","https://openalex.org/W2128518360","https://openalex.org/W2143521632","https://openalex.org/W2157958821","https://openalex.org/W2264243698","https://openalex.org/W2343213531","https://openalex.org/W2343514605","https://openalex.org/W2485514552","https://openalex.org/W2533488116","https://openalex.org/W2904845482","https://openalex.org/W2963199592","https://openalex.org/W2964161691","https://openalex.org/W2972758308","https://openalex.org/W3023496898","https://openalex.org/W4230030242","https://openalex.org/W6607574751","https://openalex.org/W6632666624","https://openalex.org/W6634233734","https://openalex.org/W6640425456","https://openalex.org/W6669386809","https://openalex.org/W6677082149","https://openalex.org/W6677246231","https://openalex.org/W6677585844","https://openalex.org/W6679567058","https://openalex.org/W6680925876","https://openalex.org/W6683152347","https://openalex.org/W6731874217","https://openalex.org/W6733924595","https://openalex.org/W6734424720","https://openalex.org/W6735374517","https://openalex.org/W6753647646","https://openalex.org/W6756615331","https://openalex.org/W6762655843"],"related_works":[],"abstract_inverted_index":{"Active":[0],"learning":[1,7],"(AL)":[2],"aims":[3],"to":[4,93,140],"maximize":[5],"the":[6,10,30,53,73,77,86,143,161,164,194,198],"performance":[8,195],"of":[9,36,69,76,89,149,155,163,169,197],"current":[11],"hypothesis":[12,31,55,82,101],"by":[13,71],"drawing":[14],"as":[15,18,43],"few":[16],"labels":[17,35,187],"possible":[19],"from":[20],"an":[21,67,90,129],"input":[22,78,91,130],"distribution.":[23,79],"Generally,":[24],"most":[25],"existing":[26],"AL":[27,135,173],"algorithms":[28],"prune":[29],"set":[32],"via":[33],"querying":[34,167,181],"unlabeled":[37],"samples":[38],"and":[39,56,121,166,185,200],"could":[40],"be":[41],"deemed":[42],"a":[44,63,95,103,115,132,147],"hypothesis-pruning":[45,199],"strategy.":[46],"However,":[47],"this":[48,125],"process":[49],"critically":[50],"depends":[51],"on":[52,106,179,193],"initial":[54],"its":[57],"subsequent":[58],"updates.":[59],"This":[60],"article":[61],"presents":[62],"distribution-shattering":[64,201],"strategy":[65],"without":[66],"estimation":[68],"hypotheses":[70],"shattering":[72],"number":[74,87,148],"density":[75,88],"For":[80],"any":[81,100],"class,":[83],"we":[84],"halve":[85],"distribution":[92,117,145],"obtain":[94],"shattered":[96,116,144],"distribution,":[97,131],"which":[98],"characterizes":[99],"with":[102,175,182],"lower":[104],"bound":[105],"VC":[107],"dimension.":[108],"Our":[109,203],"analysis":[110],"shows":[111],"that":[112],"sampling":[113],"in":[114,128,171],"reduces":[118],"label":[119],"complexity":[120],"error":[122],"disagreement.":[123],"With":[124],"paradigm":[126],"guarantee,":[127],"Shattered":[133],"Distribution-based":[134],"(SDAL)":[136],"algorithm":[137],"is":[138,205],"derived":[139],"continuously":[141],"split":[142],"into":[146],"representative":[150],"samples.":[151],"An":[152],"empirical":[153],"evaluation":[154],"benchmark":[156],"data":[157],"sets":[158],"further":[159,188],"verifies":[160],"effectiveness":[162],"halving":[165],"abilities":[168],"SDAL":[170],"real-world":[172],"tasks":[174],"limited":[176],"labels.":[177],"Experiments":[178],"active":[180],"adversarial":[183],"examples":[184],"noisy":[186],"verify":[189],"our":[190],"theoretical":[191],"insights":[192],"disagreement":[196],"strategies.":[202],"code":[204],"available":[206],"at":[207],"https://github.com/XiaofengCao-MachineLearning/Shattering-Distribution-for-Active-Learning.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2020-10-29T00:00:00"}