{"id":"https://openalex.org/W7164913011","doi":"https://doi.org/10.48550/arxiv.2606.16411","title":"Not all Jensen-Shannon Divergence Estimators are Equal","display_name":"Not all Jensen-Shannon Divergence Estimators are Equal","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164913011","doi":"https://doi.org/10.48550/arxiv.2606.16411"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16411","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16411","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16411","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111430654","display_name":"A. Garrido","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garrido, Alba","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138745721","display_name":"Alejandro Almod\u00f3var","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Almod\u00f3var, Alejandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130358560","display_name":"Mar Elizo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elizo, Mar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130344763","display_name":"Patricia A. Apell\u00e1niz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Apell\u00e1niz, Patricia A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008317106","display_name":"Santiago Zazo","orcid":"https://orcid.org/0000-0001-9073-7927"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zazo, Santiago","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5045383869","display_name":"Juan Parras","orcid":"https://orcid.org/0000-0002-7028-3179"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parras, Juan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.21660000085830688,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.21660000085830688,"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/T13309","display_name":"Reliability and Agreement in Measurement","score":0.08410000056028366,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12261","display_name":"Statistical Mechanics and Entropy","score":0.06870000064373016,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.8615000247955322},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.663100004196167},{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.43290001153945923},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.41760000586509705},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4172999858856201},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.41119998693466187},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4106999933719635},{"id":"https://openalex.org/keywords/empirical-distribution-function","display_name":"Empirical distribution function","score":0.3702999949455261},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.34290000796318054}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8615000247955322},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.663100004196167},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6121000051498413},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.43290001153945923},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.423799991607666},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4106999933719635},{"id":"https://openalex.org/C98385598","wikidata":"https://www.wikidata.org/wiki/Q1339385","display_name":"Empirical distribution function","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.34610000252723694},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.313400000333786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3077000081539154},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C164172150","wikidata":"https://www.wikidata.org/wiki/Q1782585","display_name":"Consistent estimator","level":4,"score":0.3041999936103821},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2777000069618225},{"id":"https://openalex.org/C206654554","wikidata":"https://www.wikidata.org/wiki/Q5374247","display_name":"Empirical measure","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.25380000472068787},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16411","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16411","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16411","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16411","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"Jensen-Shannon":[1,138,145,172],"divergence":[2,39,146],"is":[3,20,40],"widely":[4],"reported":[5],"as":[6],"a":[7,33,132],"scalar":[8],"measure":[9],"of":[10,154],"fidelity":[11],"for":[12,136,159,170],"synthetic":[13,104],"tabular":[14,105],"data.":[15],"Yet,":[16],"in":[17,71,112,123],"practice,":[18],"it":[19],"estimated":[21],"from":[22],"finite":[23],"samples":[24],"using":[25],"protocols":[26,62],"that":[27,60,143],"are":[28,148],"often":[29],"underspecified.":[30],"This":[31],"creates":[32],"measurement":[34],"problem.":[35],"Although":[36],"the":[37,43,48,72,155],"population":[38],"well":[41],"defined,":[42],"empirical":[44,144],"value":[45],"depends":[46],"on":[47],"estimator":[49,89,121],"family,":[50],"sampling":[51],"protocol,":[52],"calibration,":[53],"dimensionality,":[54],"and":[55,75,102,120,166],"class":[56,118],"balance.":[57],"We":[58,91,162],"show":[59,142],"different":[61],"can":[63,76],"yield":[64],"non-comparable":[65],"values:":[66],"marginal-based":[67],"estimators":[68,82],"ignore":[69],"dependencies":[70],"joint":[73,84],"distribution":[74],"severely":[77],"underestimate":[78],"divergence,":[79],"while":[80],"classifier-based":[81,137],"capture":[83],"structure":[85],"but":[86],"exhibit":[87],"strong":[88],"dependence.":[90],"systematically":[92],"study":[93],"this":[94],"behavior":[95],"across":[96],"controlled":[97],"settings":[98],"with":[99],"reference":[100],"divergences":[101],"real-world":[103],"benchmarks.":[106],"Our":[107,140],"analysis":[108],"reveals":[109],"dependence":[110],"blindness":[111],"marginal":[113],"estimators,":[114],"prior-shift":[115],"bias":[116],"under":[117],"imbalance,":[119],"sensitivity":[122],"high":[124],"dimensions.":[125],"To":[126],"address":[127],"prior":[128],"shift,":[129],"we":[130],"derive":[131],"closed-form":[133],"posterior":[134],"correction":[135],"estimation.":[139],"results":[141],"values":[147],"inherently":[149],"protocol-dependent,":[150],"making":[151],"explicit":[152],"specification":[153],"estimation":[156],"procedure":[157],"necessary":[158],"meaningful":[160],"comparison.":[161],"provide":[163],"practical":[164],"guidelines":[165],"an":[167],"open-source":[168],"tool":[169],"estimator-aware":[171],"evaluation.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-17T00:00:00"}
