{"id":"https://openalex.org/W2770992186","doi":"https://doi.org/10.1109/jstsp.2018.2846218","title":"Compression-Based Regularization With an Application to Multitask Learning","display_name":"Compression-Based Regularization With an Application to Multitask Learning","publication_year":2018,"publication_date":"2018-06-11","ids":{"openalex":"https://openalex.org/W2770992186","doi":"https://doi.org/10.1109/jstsp.2018.2846218","mag":"2770992186"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2018.2846218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2018.2846218","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1711.07099","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052132831","display_name":"Mat\u00edas Vera","orcid":"https://orcid.org/0000-0001-9180-7595"},"institutions":[{"id":"https://openalex.org/I24354313","display_name":"Universidad de Buenos Aires","ror":"https://ror.org/0081fs513","country_code":"AR","type":"education","lineage":["https://openalex.org/I24354313"]}],"countries":["AR"],"is_corresponding":false,"raw_author_name":"Matias Vera","raw_affiliation_strings":["Universidad de Buenos Aires, Buenos Aires, AR","UBA - Universidad de Buenos Aires [Buenos Aires] (Viamonte 430/44 C1053ABJ,  Ciudad de Buenos Aires - Argentine)"],"raw_orcid":"https://orcid.org/0000-0001-9180-7595","affiliations":[{"raw_affiliation_string":"Universidad de Buenos Aires, Buenos Aires, AR","institution_ids":["https://openalex.org/I24354313"]},{"raw_affiliation_string":"UBA - Universidad de Buenos Aires [Buenos Aires] (Viamonte 430/44 C1053ABJ,  Ciudad de Buenos Aires - Argentine)","institution_ids":["https://openalex.org/I24354313"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035529329","display_name":"Leonardo Rey Vega","orcid":"https://orcid.org/0000-0002-5578-0521"},"institutions":[{"id":"https://openalex.org/I24354313","display_name":"Universidad de Buenos Aires","ror":"https://ror.org/0081fs513","country_code":"AR","type":"education","lineage":["https://openalex.org/I24354313"]}],"countries":["AR"],"is_corresponding":false,"raw_author_name":"Leonardo Rey Vega","raw_affiliation_strings":["Universidad de Buenos Aires, Buenos Aires, AR","UBA - Universidad de Buenos Aires [Buenos Aires] (Viamonte 430/44 C1053ABJ,  Ciudad de Buenos Aires - Argentine)"],"raw_orcid":"https://orcid.org/0000-0002-5578-0521","affiliations":[{"raw_affiliation_string":"Universidad de Buenos Aires, Buenos Aires, AR","institution_ids":["https://openalex.org/I24354313"]},{"raw_affiliation_string":"UBA - Universidad de Buenos Aires [Buenos Aires] (Viamonte 430/44 C1053ABJ,  Ciudad de Buenos Aires - Argentine)","institution_ids":["https://openalex.org/I24354313"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071189599","display_name":"Pablo Piantanida","orcid":"https://orcid.org/0000-0002-8717-2117"},"institutions":[{"id":"https://openalex.org/I102197404","display_name":"Universit\u00e9 Paris-Sud","ror":"https://ror.org/028rypz17","country_code":"FR","type":"education","lineage":["https://openalex.org/I102197404"]},{"id":"https://openalex.org/I4210097418","display_name":"Laboratoire des signaux et syst\u00e8mes","ror":"https://ror.org/00skw9v43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I277688954","https://openalex.org/I277688954","https://openalex.org/I4210097418","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Pablo Piantanida","raw_affiliation_strings":["Universite Paris-Sud, Orsay, \u00c3\u017dle-de-France, FR","L2S - Laboratoire des signaux et syst\u00e8mes (Plateau de Moulon 3 rue Joliot Curie 91192 GIF SUR YVETTE CEDEX - France)"],"raw_orcid":"https://orcid.org/0000-0002-8717-2117","affiliations":[{"raw_affiliation_string":"Universite Paris-Sud, Orsay, \u00c3\u017dle-de-France, FR","institution_ids":["https://openalex.org/I102197404"]},{"raw_affiliation_string":"L2S - Laboratoire des signaux et syst\u00e8mes (Plateau de Moulon 3 rue Joliot Curie 91192 GIF SUR YVETTE CEDEX - France)","institution_ids":["https://openalex.org/I4210097418"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1216,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83233255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"12","issue":"5","first_page":"1063","last_page":"1076"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994000196456909,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994000196456909,"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":0.9993000030517578,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9983999729156494,"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":0.7244583368301392},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6917198896408081},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5184144973754883},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4768487215042114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46556711196899414},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.4642553925514221},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.456913560628891},{"id":"https://openalex.org/keywords/information-bottleneck-method","display_name":"Information bottleneck method","score":0.4534076750278473},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4501585066318512},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4359927475452423},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.4242875874042511},{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.42318516969680786},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.36587899923324585},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3070582449436188},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2834851145744324}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7244583368301392},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6917198896408081},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5184144973754883},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4768487215042114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46556711196899414},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.4642553925514221},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.456913560628891},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.4534076750278473},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4501585066318512},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4359927475452423},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.4242875874042511},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.42318516969680786},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36587899923324585},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3070582449436188},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2834851145744324},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/jstsp.2018.2846218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2018.2846218","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1711.07099","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1711.07099","pdf_url":"https://arxiv.org/pdf/1711.07099","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:oai:HAL:hal-02944069v1","is_oa":true,"landing_page_url":"https://centralesupelec.hal.science/hal-02944069","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing, 2018, 12 (5), pp.1063-1076. &#x27E8;10.1109/JSTSP.2018.2846218&#x27E9;","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1711.07099","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1711.07099","pdf_url":"https://arxiv.org/pdf/1711.07099","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":0.4399999976158142,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":75,"referenced_works":["https://openalex.org/W53759226","https://openalex.org/W1493526108","https://openalex.org/W1494099594","https://openalex.org/W1503398984","https://openalex.org/W1506806321","https://openalex.org/W1572659166","https://openalex.org/W1595613095","https://openalex.org/W1623027413","https://openalex.org/W1641061802","https://openalex.org/W1686946872","https://openalex.org/W1730404227","https://openalex.org/W1871863450","https://openalex.org/W2029236670","https://openalex.org/W2065180801","https://openalex.org/W2066784856","https://openalex.org/W2068660182","https://openalex.org/W2096516049","https://openalex.org/W2098145033","https://openalex.org/W2114085948","https://openalex.org/W2114315281","https://openalex.org/W2119016846","https://openalex.org/W2123469175","https://openalex.org/W2127314673","https://openalex.org/W2130903752","https://openalex.org/W2138570191","https://openalex.org/W2142901448","https://openalex.org/W2143121893","https://openalex.org/W2148986322","https://openalex.org/W2150412388","https://openalex.org/W2161405788","https://openalex.org/W2161877964","https://openalex.org/W2162888803","https://openalex.org/W2296319761","https://openalex.org/W2338970276","https://openalex.org/W2398870399","https://openalex.org/W2478708596","https://openalex.org/W2593634001","https://openalex.org/W2742079690","https://openalex.org/W2769052546","https://openalex.org/W2913340405","https://openalex.org/W2964160479","https://openalex.org/W2964184826","https://openalex.org/W3101231107","https://openalex.org/W3104240813","https://openalex.org/W3104404357","https://openalex.org/W3141797743","https://openalex.org/W4205855417","https://openalex.org/W4230960895","https://openalex.org/W4249667877","https://openalex.org/W4250589301","https://openalex.org/W4293469690","https://openalex.org/W4297749952","https://openalex.org/W4300840296","https://openalex.org/W4302609454","https://openalex.org/W6602136431","https://openalex.org/W6614676750","https://openalex.org/W6635548358","https://openalex.org/W6636743233","https://openalex.org/W6636782177","https://openalex.org/W6637108112","https://openalex.org/W6637570865","https://openalex.org/W6639248675","https://openalex.org/W6667840798","https://openalex.org/W6674415119","https://openalex.org/W6674609639","https://openalex.org/W6679063059","https://openalex.org/W6679734692","https://openalex.org/W6680765659","https://openalex.org/W6683886454","https://openalex.org/W6703913054","https://openalex.org/W6712834526","https://openalex.org/W6729906282","https://openalex.org/W6742058293","https://openalex.org/W6746402748","https://openalex.org/W6786216725"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1770458422","https://openalex.org/W2783047733","https://openalex.org/W3189092450","https://openalex.org/W3167660944","https://openalex.org/W4287238667","https://openalex.org/W3149287595","https://openalex.org/W3173203577","https://openalex.org/W4225670787","https://openalex.org/W4283805326"],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates,":[2],"from":[3,28,60,106],"information":[4,82,130,194],"theoretic":[5,83],"grounds,":[6],"a":[7,18,49,90,164,178],"learning":[8,92],"problem":[9,59,88,121],"based":[10],"on":[11,135,201],"the":[12,39,61,64,76,96,119,132,144,172,182,202,205],"principle":[13],"that":[14,189],"any":[15],"regularity":[16],"in":[17,42,94],"given":[19],"dataset":[20],"can":[21,122],"be":[22,123],"exploited":[23],"to":[24,36,44,109,162,212],"extract":[25],"compact":[26],"features":[27],"data,":[29],"i.e.,":[30],"using":[31],"fewer":[32],"bits":[33],"than":[34],"needed":[35],"fully":[37],"describe":[38],"data":[40],"itself,":[41],"order":[43],"build":[45],"meaningful":[46],"representations":[47,108],"of":[48,66,70,85,118,149,158,207],"relevant":[50],"content":[51],"(multiple":[52],"labels).":[53],"We":[54],"begin":[55],"studying":[56],"amultitask":[57],"learning(MTL)":[58],"average":[62,173],"(over":[63],"tasks)":[65],"misclassification":[67],"probability":[68],"point":[69],"view":[71],"and":[72,147,204,216],"linking":[73],"it":[74,170],"with":[75,128],"popularcross-entropycriterion.":[77],"Our":[78],"approach":[79],"allows":[80],"an":[81,86,139,192],"formulation":[84,117],"MTL":[87,120],"as":[89,125],"supervised":[91],"framework,":[93],"which":[95,199],"prediction":[97],"models":[98],"for":[99,142],"several":[100],"related":[101],"tasks":[102],"are":[103,153,220],"learned":[104],"jointly":[105],"common":[107],"achieve":[110],"better":[111],"generalization":[112],"performance.":[113],"More":[114],"precisely,":[115],"our":[116],"interpreted":[124],"aninformation":[126],"bottleneckproblem":[127],"side":[129],"at":[131],"decoder.":[133],"Based":[134],"that,":[136],"we":[137],"present":[138],"iterative":[140],"algorithm":[141,160],"computing":[143],"optimal":[145,193],"tradeoffs":[146],"some":[148],"its":[150],"convergence":[151],"properties":[152],"studied.":[154],"An":[155],"important":[156],"feature":[157],"this":[159],"is":[161],"provide":[163],"natural":[165],"safeguard":[166],"against":[167],"overfitting,":[168],"because":[169],"minimizes":[171],"risk":[174],"taking":[175],"into":[176],"account":[177],"penalization":[179],"induced":[180],"by":[181],"model":[183],"complexity.":[184],"Remarkably,":[185],"empirical":[186],"results":[187],"illustrate":[188],"there":[190],"exists":[191],"rate":[195],"minimizing":[196],"theexcess":[197],"risk,":[198],"depends":[200],"nature":[203],"amount":[206],"available":[208],"training":[209],"data.":[210],"Applications":[211],"hierarchical":[213],"text":[214],"categorization":[215],"distributional":[217],"word":[218],"clusters":[219],"also":[221],"investigated,":[222],"extending":[223],"previous":[224],"works.":[225]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2017-12-04T00:00:00"}
