{"id":"https://openalex.org/W4389747842","doi":"https://doi.org/10.1109/tpami.2023.3343359","title":"Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning","display_name":"Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning","publication_year":2023,"publication_date":"2023-12-14","ids":{"openalex":"https://openalex.org/W4389747842","doi":"https://doi.org/10.1109/tpami.2023.3343359","pmid":"https://pubmed.ncbi.nlm.nih.gov/38096091"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2023.3343359","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3343359","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2312.10116","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057879803","display_name":"Wei Tan","orcid":"https://orcid.org/0000-0002-9778-9970"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Wei Tan","raw_affiliation_strings":["Department of Data Science and AI, Monash University, Clayton, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Monash University, Clayton, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021845515","display_name":"Lan Du","orcid":"https://orcid.org/0000-0002-9925-0223"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lan Du","raw_affiliation_strings":["Department of Data Science and AI, Monash University, Clayton, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Monash University, Clayton, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005792924","display_name":"Wray Buntine","orcid":"https://orcid.org/0000-0001-9292-1015"},"institutions":[{"id":"https://openalex.org/I4210142044","display_name":"VinUniversity","ror":"https://ror.org/052dmdr17","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210142044"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Wray Buntine","raw_affiliation_strings":["VinUniversity, Gia Lam, Hanoi, Vietnam"],"affiliations":[{"raw_affiliation_string":"VinUniversity, Gia Lam, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210142044"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057879803"],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":1.5822,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.87000382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"46","issue":"5","first_page":"3463","last_page":"3479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","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/T12072","display_name":"Machine Learning and Algorithms","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9947999715805054,"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":0.9941999912261963,"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/bayesian-probability","display_name":"Bayesian probability","score":0.6039153933525085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5965061187744141},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5933260321617126},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5904780626296997},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5399518013000488},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5106309056282043},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5028464198112488},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4924331605434418},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4923290014266968},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4423504173755646},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4366210997104645},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4119790494441986},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38768741488456726},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3814702033996582},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33100295066833496},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2800407409667969}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6039153933525085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5965061187744141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5933260321617126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5904780626296997},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5399518013000488},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5106309056282043},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5028464198112488},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4924331605434418},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4923290014266968},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4423504173755646},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4366210997104645},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4119790494441986},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38768741488456726},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3814702033996582},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33100295066833496},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2800407409667969},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tpami.2023.3343359","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3343359","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:38096091","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38096091","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 pattern analysis and machine intelligence","raw_type":null},{"id":"pmh:oai:arXiv.org:2312.10116","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.10116","pdf_url":"https://arxiv.org/pdf/2312.10116","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2312.10116","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.10116","pdf_url":"https://arxiv.org/pdf/2312.10116","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7492826389","display_name":null,"funder_award_id":"1904-57761","funder_id":"https://openalex.org/F4320318819","funder_display_name":"Tides Foundation"}],"funders":[{"id":"https://openalex.org/F4320318819","display_name":"Tides Foundation","ror":"https://ror.org/05ascfq76"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":77,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1872312298","https://openalex.org/W1965387924","https://openalex.org/W1978633512","https://openalex.org/W1979685007","https://openalex.org/W1998274472","https://openalex.org/W2011674654","https://openalex.org/W2054321977","https://openalex.org/W2063863981","https://openalex.org/W2096623936","https://openalex.org/W2096765209","https://openalex.org/W2098742124","https://openalex.org/W2169027522","https://openalex.org/W2194775991","https://openalex.org/W2216309107","https://openalex.org/W2221311665","https://openalex.org/W2254249950","https://openalex.org/W2518897583","https://openalex.org/W2593890499","https://openalex.org/W2772303887","https://openalex.org/W2774918944","https://openalex.org/W2885577113","https://openalex.org/W2899575464","https://openalex.org/W2903158431","https://openalex.org/W2910982074","https://openalex.org/W2949677752","https://openalex.org/W2951911250","https://openalex.org/W2964282813","https://openalex.org/W2978017171","https://openalex.org/W2997654229","https://openalex.org/W3023176787","https://openalex.org/W3049254243","https://openalex.org/W3101345273","https://openalex.org/W3103649165","https://openalex.org/W3118608800","https://openalex.org/W3123709248","https://openalex.org/W3171605201","https://openalex.org/W3174481471","https://openalex.org/W3180217530","https://openalex.org/W3205626500","https://openalex.org/W4232528534","https://openalex.org/W4252475223","https://openalex.org/W4287867774","https://openalex.org/W6628826841","https://openalex.org/W6637373629","https://openalex.org/W6639393196","https://openalex.org/W6676984168","https://openalex.org/W6684191040","https://openalex.org/W6685053522","https://openalex.org/W6688998120","https://openalex.org/W6691459498","https://openalex.org/W6730042731","https://openalex.org/W6735374517","https://openalex.org/W6735509820","https://openalex.org/W6739651123","https://openalex.org/W6745097924","https://openalex.org/W6747231328","https://openalex.org/W6751979845","https://openalex.org/W6755709506","https://openalex.org/W6756040250","https://openalex.org/W6756615331","https://openalex.org/W6757817989","https://openalex.org/W6758233323","https://openalex.org/W6763607942","https://openalex.org/W6764588566","https://openalex.org/W6766403704","https://openalex.org/W6768851824","https://openalex.org/W6773642575","https://openalex.org/W6777385685","https://openalex.org/W6787972765","https://openalex.org/W6789528441","https://openalex.org/W6790661835","https://openalex.org/W6791080800","https://openalex.org/W6794652640","https://openalex.org/W6796982079","https://openalex.org/W6803387787","https://openalex.org/W6803499426"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W2952088488","https://openalex.org/W4225691210","https://openalex.org/W2481230473","https://openalex.org/W2792147139","https://openalex.org/W2514220927"],"abstract_inverted_index":{"The":[0,172],"effectiveness":[1],"of":[2,11,24,43,77,101,135,150,174,185],"active":[3],"learning":[4],"largely":[5],"depends":[6],"on":[7,20,122,141],"the":[8,12,25,36,50,84,99,129,133,148,169,177],"sampling":[9,192],"efficiency":[10],"acquisition":[13,86,161],"function.":[14],"Expected":[15],"Loss":[16],"Reduction":[17],"(ELR)":[18],"focuses":[19],"a":[21,90,183],"Bayesian":[22,41],"estimate":[23,49],"reduction":[26],"in":[27,35,52,98,104],"classification":[28],"error,":[29],"and":[30,117,125,144,154,163,166,197],"more":[31],"general":[32,75],"costs":[33],"fit":[34],"same":[37],"framework.":[38,67],"We":[39,68],"propose":[40],"Estimate":[42],"Mean":[44],"Proper":[45],"Scores":[46],"(BEMPS)":[47],"to":[48],"increase":[51],"strictly":[53],"proper":[54],"scores":[55,105],"such":[56],"as":[57],"log":[58,155],"probability":[59,156],"or":[60],"negative":[61],"mean":[62,151],"square":[63,152],"error":[64,153],"within":[65],"this":[66,74],"also":[69],"prove":[70],"convergence":[71],"results":[72],"for":[73,106],"class":[76],"costs.":[78],"To":[79,109],"facilitate":[80],"better":[81],"experimentation":[82],"with":[83,132,157],"new":[85],"functions,":[87],"we":[88,113,189],"develop":[89],"complementary":[91],"batch":[92],"AL":[93],"algorithm":[94],"that":[95,147],"encourages":[96],"diversity":[97],"vector":[100],"expected":[102],"changes":[103],"unlabeled":[107],"data.":[108],"allow":[110],"high-performance":[111],"classifiers,":[112,165],"combine":[114],"deep":[115],"ensembles,":[116],"dynamic":[118],"validation":[119],"set":[120,184],"construction":[121],"pretrained":[123],"models,":[124],"further":[126,180],"speed":[127],"up":[128],"ensemble":[130],"process":[131],"idea":[134],"Monte":[136],"Carlo":[137],"Dropout.":[138],"Extensive":[139],"experiments":[140],"both":[142],"texts":[143],"images":[145],"show":[146],"use":[149],"BEMPS":[158,175],"yields":[159],"robust":[160],"functions":[162],"well-calibrated":[164],"consistently":[167],"outperforms":[168],"others":[170,178],"tested.":[171],"advantages":[173],"over":[176],"are":[179],"supported":[181],"by":[182],"qualitative":[186],"analyses,":[187],"where":[188],"visualise":[190],"their":[191],"behaviour":[193],"using":[194],"data":[195],"maps":[196],"t-SNE":[198],"plots.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}