{"id":"https://openalex.org/W3028887153","doi":"https://doi.org/10.1109/tit.2020.2998577","title":"Minimum Description Length Principle in Supervised Learning With Application to Lasso","display_name":"Minimum Description Length Principle in Supervised Learning With Application to Lasso","publication_year":2020,"publication_date":"2020-05-30","ids":{"openalex":"https://openalex.org/W3028887153","doi":"https://doi.org/10.1109/tit.2020.2998577","mag":"3028887153"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2020.2998577","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tit.2020.2998577","pdf_url":"https://ieeexplore.ieee.org/ielx7/18/9120400/09103589.pdf","source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/18/9120400/09103589.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091336955","display_name":"Masanori Kawakita","orcid":"https://orcid.org/0000-0003-4224-8939"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masanori Kawakita","raw_affiliation_strings":["Graduate School of Informatics, Nagoya University, Nagoya, Japan"],"raw_orcid":"https://orcid.org/0000-0003-4224-8939","affiliations":[{"raw_affiliation_string":"Graduate School of Informatics, Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020270107","display_name":"Jun\u2019ichi Takeuchi","orcid":"https://orcid.org/0000-0002-5819-3082"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jun'ichi Takeuchi","raw_affiliation_strings":["Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan"],"raw_orcid":"https://orcid.org/0000-0002-5819-3082","affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7203,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65291509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"66","issue":"7","first_page":"4245","last_page":"4269"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-description-length","display_name":"Minimum description length","score":0.9021308422088623},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.629300594329834},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5903288125991821},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44908517599105835},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.43987423181533813},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4303542375564575},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.42971402406692505},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39750826358795166},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3482748866081238},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25491777062416077},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15291574597358704}],"concepts":[{"id":"https://openalex.org/C87465248","wikidata":"https://www.wikidata.org/wiki/Q1417790","display_name":"Minimum description length","level":2,"score":0.9021308422088623},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.629300594329834},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5903288125991821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44908517599105835},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.43987423181533813},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4303542375564575},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.42971402406692505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39750826358795166},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3482748866081238},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25491777062416077},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15291574597358704},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tit.2020.2998577","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tit.2020.2998577","pdf_url":"https://ieeexplore.ieee.org/ielx7/18/9120400/09103589.pdf","source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/tit.2020.2998577","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tit.2020.2998577","pdf_url":"https://ieeexplore.ieee.org/ielx7/18/9120400/09103589.pdf","source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5199999809265137,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G4418404237","display_name":"Development of the MDL Principle and Its Applications","funder_award_id":"18H03291","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5006988534","display_name":"Variable Selection for Small Sample and High Dimension Case by Semi-supervised Learning and Its Application to Super-Resolution","funder_award_id":"25870503","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320322617","display_name":"Okawa Foundation for Information and Telecommunications","ror":"https://ror.org/01enbtr31"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3028887153.pdf","grobid_xml":"https://content.openalex.org/works/W3028887153.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W349256592","https://openalex.org/W1554032699","https://openalex.org/W1554944419","https://openalex.org/W1573082642","https://openalex.org/W1573613494","https://openalex.org/W2004686034","https://openalex.org/W2023824537","https://openalex.org/W2026653933","https://openalex.org/W2028069051","https://openalex.org/W2034260606","https://openalex.org/W2037430361","https://openalex.org/W2045756756","https://openalex.org/W2054658115","https://openalex.org/W2056957301","https://openalex.org/W2064437806","https://openalex.org/W2078645667","https://openalex.org/W2086333522","https://openalex.org/W2095734615","https://openalex.org/W2100556411","https://openalex.org/W2101460669","https://openalex.org/W2102098892","https://openalex.org/W2114529990","https://openalex.org/W2116581043","https://openalex.org/W2117146852","https://openalex.org/W2122882636","https://openalex.org/W2123202508","https://openalex.org/W2126166357","https://openalex.org/W2130342952","https://openalex.org/W2132555912","https://openalex.org/W2135046866","https://openalex.org/W2148333713","https://openalex.org/W2150070703","https://openalex.org/W2168029744","https://openalex.org/W2171033594","https://openalex.org/W2285744368","https://openalex.org/W2478708596","https://openalex.org/W2593087291","https://openalex.org/W2775405960","https://openalex.org/W3027700053","https://openalex.org/W3099718522","https://openalex.org/W3101040439","https://openalex.org/W3101749733","https://openalex.org/W3104148512","https://openalex.org/W3105340263","https://openalex.org/W3106224380","https://openalex.org/W3110683067","https://openalex.org/W3123545922","https://openalex.org/W4235825416","https://openalex.org/W4253654031","https://openalex.org/W4323347026","https://openalex.org/W6677462471","https://openalex.org/W6678474271","https://openalex.org/W6679508761","https://openalex.org/W6695876249","https://openalex.org/W6786768871"],"related_works":["https://openalex.org/W4298831272","https://openalex.org/W2962916388","https://openalex.org/W2086694237","https://openalex.org/W2095614499","https://openalex.org/W2489956408","https://openalex.org/W4390062853","https://openalex.org/W4389256085","https://openalex.org/W4399290976","https://openalex.org/W4285328440","https://openalex.org/W4313644201"],"abstract_inverted_index":{"The":[0,11,160,280],"minimum":[1],"description":[2,20],"length":[3],"(MDL)":[4],"principle":[5,13,41,54],"is":[6,14,42,86,143,153,163,183,189,193,210,238,253,324,333],"extended":[7],"to":[8,25,72,101,105,196,200,225,248,340],"supervised":[9,177,226,341],"learning.":[10,159],"MDL":[12,40,53,131,268],"a":[15,148,217,249],"philosophy":[16],"that":[17,119,144,255,271,331],"the":[18,26,30,35,39,52,65,73,120,155,180,201,207,244,258,274,292,299,321,334],"shortest":[19],"of":[21,34,67,76,82,90,205,235,277,320,337],"given":[22],"data":[23,31],"leads":[24],"best":[27],"hypothesis":[28],"about":[29],"source.":[32],"One":[33],"key":[36],"theories":[37],"for":[38,169,176,221,302],"Barron":[43,96],"and":[44,95,133,232,270,308],"Cover's":[45],"theory":[46,104,199,224,339],"(BC":[47],"theory),":[48],"which":[49,152],"mathematically":[50],"justifies":[51],"based":[55],"on":[56],"two-stage":[57,68],"codes":[58,69],"in":[59,257],"density":[60],"estimation":[61],"(unsupervised":[62],"learning).":[63],"Though":[64],"codelength":[66],"looks":[70],"similar":[71],"target":[74],"function":[75],"penalized":[77,83,106,122],"likelihood":[78,84,107,123],"methods,":[79],"parameter":[80,91,208,236],"optimization":[81],"methods":[85,108],"done":[87],"without":[88],"quantization":[89,234],"space.":[92],"Recently,":[93],"Chatterjee":[94],"have":[97,284],"provided":[98],"theoretical":[99,219],"tools":[100],"extend":[102,197],"BC":[103,138,198,223,278,289,338],"by":[109,137,326],"overcoming":[110],"this":[111,215,241,332],"difference.":[112],"Indeed,":[113],"applying":[114],"their":[115,145],"tools,":[116],"they":[117],"showed":[118],"famous":[121],"method":[124],"`lasso'":[125],"can":[126,262],"be":[127,264],"interpreted":[128,265],"as":[129,157,266],"an":[130,267],"estimator":[132],"enjoys":[134,273],"performance":[135],"guarantee":[136],"theory.":[139,279,290],"An":[140],"important":[141],"fact":[142],"results":[146],"assume":[147],"fixed":[149,161,181],"design":[150,162,182,188,203,230,260],"setting,":[151],"essentially":[154],"same":[156],"unsupervised":[158],"natural":[164],"if":[165,315],"we":[166,173],"use":[167,174],"lasso":[168,175,256,272],"compressed":[170],"sensing.":[171],"If":[172],"learning,":[178],"however,":[179],"considerably":[184],"unsatisfactory.":[185],"Only":[186],"random":[187,202,229,259],"acceptable.":[190],"However,":[191],"it":[192,252],"inherently":[194],"difficult":[195],"regardless":[204],"whether":[206],"space":[209],"quantized":[211],"or":[212],"not.":[213],"In":[214],"paper,":[216],"novel":[218],"tool":[220],"extending":[222],"learning":[227,342],"(the":[228],"setting":[231,261],"no":[233],"space)":[237],"provided.":[239],"Applying":[240],"tool,":[242],"when":[243],"covariates":[245],"are":[246],"subject":[247],"Gaussian":[250],"distribution,":[251],"proved":[254],"also":[263],"estimator,":[269],"risk":[275],"bound":[276,323],"risk/regret":[281],"bounds":[282,293,300],"obtained":[283],"several":[285],"advantages":[286],"inherited":[287],"from":[288],"First,":[291],"require":[294],"remarkably":[295],"few":[296],"assumptions.":[297],"Second,":[298],"hold":[301],"any":[303,309],"finite":[304,310],"sample":[305],"size":[306],"$n$":[307],"feature":[311],"number":[312],"$p$":[313],"even":[314],"$n\\ll":[316],"p$":[317],".":[318],"Behavior":[319],"regret":[322],"investigated":[325],"numerical":[327],"simulations.":[328],"We":[329],"believe":[330],"first":[335],"extensions":[336],"(random":[343],"design).":[344]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-10-10T00:00:00"}
