{"id":"https://openalex.org/W2974929241","doi":"https://doi.org/10.1109/isit.2019.8849414","title":"Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions","display_name":"Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2974929241","doi":"https://doi.org/10.1109/isit.2019.8849414","mag":"2974929241"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2019.8849414","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/1721.1/137042","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071112095","display_name":"Ziv Goldfeld","orcid":"https://orcid.org/0000-0003-3406-3950"},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Ziv Goldfeld","raw_affiliation_strings":["MIT","IBM Research"],"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]},{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002525343","display_name":"Kristjan Greenewald","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kristjan Greenewald","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023292380","display_name":"Jonathan Weed","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Jonathan Weed","raw_affiliation_strings":["MIT","IBM Research"],"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]},{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031031216","display_name":"Yury Polyanskiy","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Yury Polyanskiy","raw_affiliation_strings":["MIT","IBM Research"],"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]},{"raw_affiliation_string":"IBM Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5071112095"],"corresponding_institution_ids":["https://openalex.org/I4210109586"],"apc_list":null,"apc_paid":null,"fwci":1.1932,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.81657509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"892","last_page":"896"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9940000176429749,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9940000176429749,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9901000261306763,"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":0.9894999861717224,"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/estimator","display_name":"Estimator","score":0.7914290428161621},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6442364454269409},{"id":"https://openalex.org/keywords/entropy-estimation","display_name":"Entropy estimation","score":0.6418205499649048},{"id":"https://openalex.org/keywords/differential-entropy","display_name":"Differential entropy","score":0.5407500267028809},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.4837246239185333},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.44377604126930237},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.44345730543136597},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.4315636157989502},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34270384907722473},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31973862648010254},{"id":"https://openalex.org/keywords/binary-entropy-function","display_name":"Binary entropy function","score":0.17969480156898499},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.17200931906700134},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10332271456718445}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7914290428161621},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6442364454269409},{"id":"https://openalex.org/C95546049","wikidata":"https://www.wikidata.org/wiki/Q1345207","display_name":"Entropy estimation","level":3,"score":0.6418205499649048},{"id":"https://openalex.org/C124551494","wikidata":"https://www.wikidata.org/wiki/Q3055345","display_name":"Differential entropy","level":4,"score":0.5407500267028809},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.4837246239185333},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.44377604126930237},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.44345730543136597},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4315636157989502},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34270384907722473},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31973862648010254},{"id":"https://openalex.org/C44415725","wikidata":"https://www.wikidata.org/wiki/Q4913893","display_name":"Binary entropy function","level":3,"score":0.17969480156898499},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.17200931906700134},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10332271456718445},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isit.2019.8849414","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},{"id":"pmh:oai:dspace.mit.edu:1721.1/137042","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/137042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"MIT web domain","raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/137042","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/137042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"MIT web domain","raw_type":"http://purl.org/eprint/type/ConferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1538444754","https://openalex.org/W1983565400","https://openalex.org/W1989008275","https://openalex.org/W2005560667","https://openalex.org/W2052044664","https://openalex.org/W2092939357","https://openalex.org/W2114771311","https://openalex.org/W2131172946","https://openalex.org/W2142901448","https://openalex.org/W2188155116","https://openalex.org/W2303810353","https://openalex.org/W2593634001","https://openalex.org/W2767895699","https://openalex.org/W2806801315","https://openalex.org/W2898398567","https://openalex.org/W2962807446","https://openalex.org/W2963180359","https://openalex.org/W2963277514","https://openalex.org/W2963608890","https://openalex.org/W2965497096","https://openalex.org/W3110763182","https://openalex.org/W4297749952","https://openalex.org/W6632302721","https://openalex.org/W6679168169","https://openalex.org/W6745777449","https://openalex.org/W6755524542"],"related_works":["https://openalex.org/W109499086","https://openalex.org/W26301252","https://openalex.org/W2962687584","https://openalex.org/W1541425763","https://openalex.org/W4296807070","https://openalex.org/W1895696520","https://openalex.org/W2782943044","https://openalex.org/W2219726305","https://openalex.org/W4295923504","https://openalex.org/W2098330974"],"abstract_inverted_index":{"This":[0,127],"paper":[1],"establishes":[2],"the":[3,6,10,22,25,81,107,111,123,129,132,136,144,147,183],"optimality":[4,130],"of":[5,12,24,55,84,110,131,146,171,182],"plugin":[7],"estimator":[8,88,113,134,149],"for":[9,93,135],"problem":[11,95],"differential":[13,26,153],"entropy":[14,27,154],"estimation":[15,23,125,160],"under":[16],"Gaussian":[17],"convolutions.":[18],"Specifically,":[19],"we":[20],"consider":[21],"h(X":[28],"+":[29,173],"Z),":[30],"where":[31],"X":[32,56,172],"and":[33,59,68,114],"Z":[34,41],"are":[35,74],"independent":[36],"d-dimensional":[37],"random":[38],"variables":[39],"with":[40],"~":[42],"N(0,":[43],"\u03c3":[44],"<sup":[45,49],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[46,50],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[47],"I":[48],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">d</sup>":[51],").":[52],"The":[53],"distribution":[54],"is":[57],"unknown":[58],"belongs":[60],"to":[61,150,169,185],"some":[62],"nonparametric":[63],"class,":[64],"but":[65],"n":[66],"independently":[67],"identically":[69],"distributed":[70],"samples":[71,170],"from":[72],"it":[73,117],"available.":[75],"We":[76,104,139],"first":[77],"show":[78,115],"that":[79,116],"despite":[80],"regularizing":[82],"effect":[83],"noise,":[85],"any":[86],"good":[87],"(within":[89],"an":[90,98],"additive":[91],"gap)":[92],"this":[94],"must":[96],"have":[97],"exponential":[99],"in":[100],"d":[101],"sample":[102],"complexity.":[103],"then":[105],"analyze":[106],"absolute-error":[108],"risk":[109],"plug-in":[112,133,148,184],"converges":[118],"as":[119],"cd\u221a/n,":[120],"thus":[121],"attaining":[122],"parametric":[124],"rate.":[126],"implies":[128],"considered":[137],"problem.":[138],"provide":[140],"numerical":[141],"results":[142,176],"comparing":[143],"performance":[145],"general-purpose":[151],"(unstructured)":[152],"estimators":[155],"(based":[156],"on":[157],"kernel":[158],"density":[159],"(KDE)":[161],"or":[162],"k":[163],"nearest":[164],"neighbors":[165],"(kNN)":[166],"techniques)":[167],"applied":[168],"Z.":[174],"These":[175],"reveal":[177],"a":[178],"significant":[179],"empirical":[180],"superiority":[181],"state-of-the-art":[186],"KDEand":[187],"kNN-based":[188],"methods.":[189]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}