{"id":"https://openalex.org/W3005443129","doi":"https://doi.org/10.1109/isit44484.2020.9174162","title":"R\u00e9nyi Entropy Bounds on the Active Learning Cost-Performance Tradeoff","display_name":"R\u00e9nyi Entropy Bounds on the Active Learning Cost-Performance Tradeoff","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3005443129","doi":"https://doi.org/10.1109/isit44484.2020.9174162","mag":"3005443129"},"language":"en","primary_location":{"id":"doi:10.1109/isit44484.2020.9174162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit44484.2020.9174162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2002.02025","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087843323","display_name":"Vahid Jamali","orcid":"https://orcid.org/0000-0003-3920-7415"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Vahid Jamali","raw_affiliation_strings":["University of Erlangen-Nuremberg,Dept. of Electrical Engineering,Germany"],"affiliations":[{"raw_affiliation_string":"University of Erlangen-Nuremberg,Dept. of Electrical Engineering,Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066992345","display_name":"Antonia M. Tulino","orcid":"https://orcid.org/0000-0002-6050-4150"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Antonia Tulino","raw_affiliation_strings":["University of Napoli Federico II,Dept. of Electrical Engineering,Italy"],"affiliations":[{"raw_affiliation_string":"University of Napoli Federico II,Dept. of Electrical Engineering,Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036654426","display_name":"Jaime Llorca","orcid":"https://orcid.org/0000-0002-6713-5861"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jaime Llorca","raw_affiliation_strings":["New York University,Tandon School of Engineering,New York"],"affiliations":[{"raw_affiliation_string":"New York University,Tandon School of Engineering,New York","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084526367","display_name":"Elza Erkip","orcid":"https://orcid.org/0000-0001-8718-8648"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elza Erkip","raw_affiliation_strings":["New York University,Tandon School of Engineering,New York"],"affiliations":[{"raw_affiliation_string":"New York University,Tandon School of Engineering,New York","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5087843323"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01687188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2807","last_page":"2812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":1.0,"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":1.0,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9948999881744385,"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":0.989300012588501,"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":0.6686801910400391},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6531664133071899},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5933635234832764},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5717642307281494},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5605072975158691},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.5296444892883301},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.498781681060791},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.493401437997818},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.49250707030296326},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.483207106590271},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4269248843193054},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.41311711072921753},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0756881833076477}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6686801910400391},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6531664133071899},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5933635234832764},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5717642307281494},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5605072975158691},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.5296444892883301},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.498781681060791},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.493401437997818},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.49250707030296326},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.483207106590271},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4269248843193054},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41311711072921753},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0756881833076477},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/isit44484.2020.9174162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit44484.2020.9174162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.02025","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.02025","pdf_url":"https://arxiv.org/pdf/2002.02025","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3005443129","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2002.02025.pdf","pdf_url":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2002.02025","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2002.02025","pdf_url":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2002.02025","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.02025","pdf_url":"https://arxiv.org/pdf/2002.02025","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3005443129.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1875754469","https://openalex.org/W2017199625","https://openalex.org/W2017803598","https://openalex.org/W2093663313","https://openalex.org/W2107131609","https://openalex.org/W2127816222","https://openalex.org/W2136504847","https://openalex.org/W2137177052","https://openalex.org/W2170057991","https://openalex.org/W2333549353","https://openalex.org/W2378745074","https://openalex.org/W2407536635","https://openalex.org/W2441269247","https://openalex.org/W2626235006","https://openalex.org/W2726539084","https://openalex.org/W2783662822","https://openalex.org/W2914331073","https://openalex.org/W2962719046","https://openalex.org/W2962729227","https://openalex.org/W2963264680","https://openalex.org/W2963451384","https://openalex.org/W2963984243","https://openalex.org/W2964310610","https://openalex.org/W2977688136","https://openalex.org/W2998244479","https://openalex.org/W3012902085","https://openalex.org/W3104381963","https://openalex.org/W4206723194","https://openalex.org/W6678881507","https://openalex.org/W6680625174","https://openalex.org/W6685083254","https://openalex.org/W6688349277","https://openalex.org/W6747484483","https://openalex.org/W6750221675","https://openalex.org/W6760359987","https://openalex.org/W6786062206"],"related_works":["https://openalex.org/W3080465292","https://openalex.org/W2293363371","https://openalex.org/W2134767644","https://openalex.org/W2922131323","https://openalex.org/W2157981294","https://openalex.org/W2606033654","https://openalex.org/W2594608700","https://openalex.org/W2901736302","https://openalex.org/W2121842036","https://openalex.org/W2135733005","https://openalex.org/W3089036689","https://openalex.org/W3210594380","https://openalex.org/W2167257437","https://openalex.org/W2122956714","https://openalex.org/W2950854713","https://openalex.org/W2015943191","https://openalex.org/W2961042725","https://openalex.org/W3094286731","https://openalex.org/W3129874696","https://openalex.org/W3040735907"],"abstract_inverted_index":{"Semi-supervised":[0],"classification,":[1],"one":[2],"of":[3,18,42,64,87,99,110],"the":[4,15,19,25,40,45,61,65,84,88,100,105,124],"most":[5],"prominent":[6],"fields":[7],"in":[8,30,97],"machine":[9],"learning,":[10],"studies":[11],"how":[12],"to":[13,32,47,52,113],"combine":[14],"statistical":[16],"knowledge":[17],"often":[20,26],"abundant":[21],"unlabeled":[22],"data":[23,29,46,111],"with":[24,71],"limited":[27],"labeled":[28,49,74],"order":[31],"maximize":[33],"overall":[34,117],"classification":[35,70,80,95,118],"accuracy.":[36,119],"In":[37,56],"this":[38,57],"context,":[39],"process":[41],"actively":[43,72],"choosing":[44],"be":[48,114],"is":[50],"referred":[51],"as":[53],"active":[54,91,134],"learning.":[55],"paper,":[58],"we":[59,82,127],"initiate":[60],"non-asymptotic":[62],"analysis":[63],"optimal":[66,90],"policy":[67],"for":[68],"semi-supervised":[69,94],"obtained":[73],"data.":[75],"Considering":[76],"a":[77],"general":[78],"Bayesian":[79],"model,":[81],"provide":[83],"first":[85],"characterization":[86],"jointly":[89],"learning":[92,135],"and":[93,116],"policy,":[96],"terms":[98],"cost-performance":[101,136],"tradeoff":[102],"driven":[103],"by":[104],"label":[106],"query":[107],"budget":[108],"(number":[109],"items":[112],"labeled)":[115],"Leveraging":[120],"recent":[121],"results":[122],"on":[123,132],"R\u00e9nyi":[125],"Entropy,":[126],"derive":[128],"tight":[129],"information-theoretic":[130],"bounds":[131],"such":[133],"tradeoff.":[137]},"counts_by_year":[],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
