{"id":"https://openalex.org/W2561586961","doi":"https://doi.org/10.1109/jsait.2020.3041036","title":"Minimax Estimation of Divergences Between Discrete Distributions","display_name":"Minimax Estimation of Divergences Between Discrete Distributions","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W2561586961","doi":"https://doi.org/10.1109/jsait.2020.3041036","mag":"2561586961"},"language":"en","primary_location":{"id":"doi:10.1109/jsait.2020.3041036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsait.2020.3041036","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"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 on Selected Areas in Information Theory","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1605.09124","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yanjun Han","orcid":"https://orcid.org/0000-0002-8335-2364"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yanjun Han","raw_affiliation_strings":["Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiantao Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiantao Jiao","raw_affiliation_strings":["University of California at Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California at Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":null,"display_name":"Tsachy Weissman","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tsachy Weissman","raw_affiliation_strings":["Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":1.4505,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.82793246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"1","issue":"3","first_page":"814","last_page":"823"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.15919999778270721,"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"}},"topics":[{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.15919999778270721,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.11879999935626984,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.07729999721050262,"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/minimax","display_name":"Minimax","score":0.8341000080108643},{"id":"https://openalex.org/keywords/minimax-estimator","display_name":"Minimax estimator","score":0.7850000262260437},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7199000120162964},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5580000281333923},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4693000018596649},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.44190001487731934},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.3862999975681305},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.385699987411499},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.35359999537467957},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.34869998693466187}],"concepts":[{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.8341000080108643},{"id":"https://openalex.org/C133939421","wikidata":"https://www.wikidata.org/wiki/Q6865379","display_name":"Minimax estimator","level":4,"score":0.7850000262260437},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7411999702453613},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7199000120162964},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5677000284194946},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5580000281333923},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4693000018596649},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4528999924659729},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.44190001487731934},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.385699987411499},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C97137487","wikidata":"https://www.wikidata.org/wiki/Q729138","display_name":"Integer (computer science)","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C117148685","wikidata":"https://www.wikidata.org/wiki/Q6865376","display_name":"Minimax approximation algorithm","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C38689907","wikidata":"https://www.wikidata.org/wiki/Q6059509","display_name":"Invariant estimator","level":5,"score":0.3098999857902527},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C35594927","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficient estimator","level":4,"score":0.29100000858306885},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C164172150","wikidata":"https://www.wikidata.org/wiki/Q1782585","display_name":"Consistent estimator","level":4,"score":0.2775999903678894},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C4285052","wikidata":"https://www.wikidata.org/wiki/Q3406268","display_name":"Moment problem","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.27410000562667847},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.27129998803138733},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.2671999931335449},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C84839998","wikidata":"https://www.wikidata.org/wiki/Q5249245","display_name":"Decision rule","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jsait.2020.3041036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsait.2020.3041036","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"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 on Selected Areas in Information Theory","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1605.09124","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.09124","pdf_url":"https://arxiv.org/pdf/1605.09124","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1605.09124","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.09124","pdf_url":"https://arxiv.org/pdf/1605.09124","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1533375765","https://openalex.org/W1535258871","https://openalex.org/W1568559199","https://openalex.org/W1854990296","https://openalex.org/W1965555277","https://openalex.org/W1982918157","https://openalex.org/W1989151402","https://openalex.org/W2000163531","https://openalex.org/W2001150023","https://openalex.org/W2021531548","https://openalex.org/W2022852240","https://openalex.org/W2030562249","https://openalex.org/W2045656688","https://openalex.org/W2049633694","https://openalex.org/W2062817469","https://openalex.org/W2078729747","https://openalex.org/W2105808439","https://openalex.org/W2114771311","https://openalex.org/W2127090196","https://openalex.org/W2129058877","https://openalex.org/W2149268774","https://openalex.org/W2150879893","https://openalex.org/W2166944917","https://openalex.org/W2518306676","https://openalex.org/W2545606300","https://openalex.org/W2554091414","https://openalex.org/W2762559060","https://openalex.org/W2885909889","https://openalex.org/W2951409584","https://openalex.org/W2963017284","https://openalex.org/W2963211500","https://openalex.org/W2963305780","https://openalex.org/W2963608890","https://openalex.org/W2963609603","https://openalex.org/W2964121406","https://openalex.org/W3049253527","https://openalex.org/W3127518054","https://openalex.org/W4302366875","https://openalex.org/W6639370665","https://openalex.org/W6678263215","https://openalex.org/W6678814708","https://openalex.org/W6742078373","https://openalex.org/W6745777449","https://openalex.org/W6745831180","https://openalex.org/W6748177128","https://openalex.org/W6762876261","https://openalex.org/W6766042122","https://openalex.org/W6767190643","https://openalex.org/W6771101475","https://openalex.org/W6781172032"],"related_works":[],"abstract_inverted_index":{"We":[0],"study":[1],"the":[2,17,21,31,40,76,87],"minimax":[3,42],"estimation":[4],"of":[5,56],"\u03b1-divergences":[6],"between":[7,95],"discrete":[8],"distributions":[9],"for":[10],"integer":[11],"\u03b1":[12],"\u2265":[13],"1,":[14],"which":[15,45,65],"include":[16],"Kullback-Leibler":[18],"divergence":[19],"and":[20,79],"\u03c7":[22],"<sup":[23],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[24],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[25],"-divergences":[26],"as":[27],"special":[28],"examples.":[29],"Dropping":[30],"usual":[32],"theoretical":[33],"tricks":[34],"to":[35],"acquire":[36],"independence,":[37],"we":[38],"construct":[39],"first":[41],"rate-optimal":[43],"estimator":[44,60,85],"does":[46],"not":[47],"require":[48],"any":[49],"Poissonization,":[50],"sample":[51],"splitting,":[52],"or":[53],"explicit":[54],"construction":[55],"approximating":[57],"polynomials.":[58],"The":[59],"uses":[61],"a":[62,67,81,91],"hybrid":[63],"approach":[64],"solves":[66],"problemindependent":[68],"linear":[69],"program":[70],"based":[71],"on":[72],"moment":[73],"matching":[74],"in":[75,86],"non-smooth":[77],"regime,":[78,89],"applies":[80],"problem-dependent":[82],"biascorrected":[83],"plug-in":[84],"smooth":[88],"with":[90],"soft":[92],"decision":[93],"boundary":[94],"these":[96],"regimes.":[97]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2017-01-06T00:00:00"}
