{"id":"https://openalex.org/W2963893615","doi":"https://doi.org/10.1109/ijcnn.2016.7727473","title":"A Robust UCB scheme for active learning in regression from strategic crowds","display_name":"A Robust UCB scheme for active learning in regression from strategic crowds","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2963893615","doi":"https://doi.org/10.1109/ijcnn.2016.7727473","mag":"2963893615"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727473","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5062089148","display_name":"Divya Padmanabhan","orcid":"https://orcid.org/0000-0002-2287-0440"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Divya Padmanabhan","raw_affiliation_strings":["Indian Institute of Science, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085428253","display_name":"Satyanath Bhat","orcid":"https://orcid.org/0000-0001-6913-4494"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Satyanath Bhat","raw_affiliation_strings":["Indian Institute of Science, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109547753","display_name":"Dinesh Garg","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dinesh Garg","raw_affiliation_strings":["IBM India Research Labs, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM India Research Labs, Bangalore","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026126465","display_name":"Shirish Shevade","orcid":"https://orcid.org/0009-0009-7202-6860"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shirish Shevade","raw_affiliation_strings":["Indian Institute of Science, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064786558","display_name":"Y. Narahari","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Y. Narahari","raw_affiliation_strings":["Indian Institute of Science, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4416,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.82504777,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"2212","last_page":"2219"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.7823643684387207},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.6373702883720398},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.576602041721344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.574284017086029},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4548414945602417},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44445621967315674},{"id":"https://openalex.org/keywords/empirical-risk-minimization","display_name":"Empirical risk minimization","score":0.42306482791900635},{"id":"https://openalex.org/keywords/budget-constraint","display_name":"Budget constraint","score":0.4174570143222809},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.37777626514434814},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11381310224533081}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7823643684387207},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.6373702883720398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.576602041721344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.574284017086029},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4548414945602417},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44445621967315674},{"id":"https://openalex.org/C107321475","wikidata":"https://www.wikidata.org/wiki/Q5374254","display_name":"Empirical risk minimization","level":2,"score":0.42306482791900635},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.4174570143222809},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37777626514434814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11381310224533081},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C133425853","wikidata":"https://www.wikidata.org/wiki/Q60571","display_name":"Neoclassical economics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727473","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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":51,"referenced_works":["https://openalex.org/W79574811","https://openalex.org/W1484084878","https://openalex.org/W1506806321","https://openalex.org/W1568139284","https://openalex.org/W1876450111","https://openalex.org/W1914338220","https://openalex.org/W1983916796","https://openalex.org/W1984332158","https://openalex.org/W1993615002","https://openalex.org/W2076580309","https://openalex.org/W2080021732","https://openalex.org/W2110454495","https://openalex.org/W2115305054","https://openalex.org/W2115979064","https://openalex.org/W2121200917","https://openalex.org/W2128678390","https://openalex.org/W2133481610","https://openalex.org/W2134305421","https://openalex.org/W2140115545","https://openalex.org/W2147004922","https://openalex.org/W2160828669","https://openalex.org/W2164215566","https://openalex.org/W2168396085","https://openalex.org/W2168405694","https://openalex.org/W2394968026","https://openalex.org/W2570764145","https://openalex.org/W2903158431","https://openalex.org/W2949071206","https://openalex.org/W2950929549","https://openalex.org/W2963246254","https://openalex.org/W3103871524","https://openalex.org/W3120740533","https://openalex.org/W4245086718","https://openalex.org/W4293877626","https://openalex.org/W6603253127","https://openalex.org/W6628826841","https://openalex.org/W6639248380","https://openalex.org/W6639923552","https://openalex.org/W6676534236","https://openalex.org/W6677547669","https://openalex.org/W6679227803","https://openalex.org/W6679679788","https://openalex.org/W6679959949","https://openalex.org/W6681011923","https://openalex.org/W6681922842","https://openalex.org/W6684421740","https://openalex.org/W6684687462","https://openalex.org/W6711814783","https://openalex.org/W6756615331","https://openalex.org/W7073710283","https://openalex.org/W7075680496"],"related_works":["https://openalex.org/W2971351794","https://openalex.org/W4376155396","https://openalex.org/W1947085858","https://openalex.org/W2174986909","https://openalex.org/W2527791220","https://openalex.org/W2101991911","https://openalex.org/W2155070487","https://openalex.org/W4311589891","https://openalex.org/W3123835761","https://openalex.org/W118270247"],"abstract_inverted_index":{"We":[0,23,99,156],"study":[1],"the":[2,42,48,62,84,105,110,118,121,124,127,131,145,154,162],"problem":[3],"of":[4,44,64,67,86,120,123],"training":[5],"an":[6,52,88,94],"accurate":[7],"linear":[8,29],"regression":[9,30],"model":[10,27,160],"by":[11],"procuring":[12],"labels":[13,45],"from":[14,47],"multiple":[15],"noisy":[16],"crowd":[17],"annotators,":[18,49],"under":[19],"a":[20,25,101],"budget":[21],"constraint.":[22],"propose":[24],"Bayesian":[26],"for":[28,37],"in":[31,113,177],"crowdsourcing":[32],"and":[33,73,92,109,168],"use":[34,130],"variational":[35],"inference":[36],"parameter":[38],"estimation.":[39],"To":[40],"minimize":[41],"number":[43],"crowdsourced":[46],"we":[50,60,78,81,129],"adopt":[51],"active":[53,68,114],"learning":[54,69],"approach.":[55],"In":[56],"this":[57],"specific":[58],"context,":[59],"prove":[61],"equivalence":[63],"well-studied":[65],"criteria":[66],"like":[70],"entropy":[71],"minimization":[72],"expected":[74],"error":[75],"reduction.":[76],"Interestingly,":[77],"observe":[79,100],"that":[80],"can":[82],"decouple":[83],"problems":[85],"identifying":[87,93],"optimal":[89],"unlabeled":[90],"instance":[91],"annotator":[95,111,146],"to":[96,117,143,161,172,175],"label":[97],"it.":[98],"useful":[102],"connection":[103],"between":[104],"multi-armed":[106],"bandit":[107],"framework":[108],"selection":[112,147],"learning.":[115],"Due":[116],"nature":[119],"distribution":[122],"rewards":[125],"on":[126,153],"arms,":[128],"Robust":[132],"Upper":[133],"Confidence":[134],"Bound":[135],"(UCB)":[136],"scheme":[137],"with":[138],"truncated":[139],"empirical":[140],"mean":[141],"estimator":[142],"solve":[144],"problem.":[148],"This":[149],"yields":[150],"provable":[151],"guarantees":[152],"regret.":[155],"further":[157],"apply":[158],"our":[159],"scenario":[163],"where":[164],"annotators":[165],"are":[166],"strategic":[167],"design":[169],"suitable":[170],"incentives":[171],"induce":[173],"them":[174],"put":[176],"their":[178],"best":[179],"efforts.":[180]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}