{"id":"https://openalex.org/W4415368755","doi":"https://doi.org/10.1109/isit63088.2025.11195446","title":"Risk-Aware Estimation from Compressed Data Beyond the Bayes Risk","display_name":"Risk-Aware Estimation from Compressed Data Beyond the Bayes Risk","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4415368755","doi":"https://doi.org/10.1109/isit63088.2025.11195446"},"language":null,"primary_location":{"id":"doi:10.1109/isit63088.2025.11195446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038057850","display_name":"Malcolm Egan","orcid":"https://orcid.org/0000-0003-2534-2018"},"institutions":[{"id":"https://openalex.org/I48430043","display_name":"Institut National des Sciences Appliqu\u00e9es de Lyon","ror":"https://ror.org/050jn9y42","country_code":"FR","type":"education","lineage":["https://openalex.org/I203339264","https://openalex.org/I48430043"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Malcolm Egan","raw_affiliation_strings":["Inria, INSA Lyon, CITI, UR3720,Villeurbanne,France"],"affiliations":[{"raw_affiliation_string":"Inria, INSA Lyon, CITI, UR3720,Villeurbanne,France","institution_ids":["https://openalex.org/I48430043"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5038057850"],"corresponding_institution_ids":["https://openalex.org/I48430043"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37533224,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.974399983882904,"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":0.9584000110626221,"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/measure","display_name":"Measure (data warehouse)","score":0.5670999884605408},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5422000288963318},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.5202999711036682},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5085999965667725},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5063999891281128},{"id":"https://openalex.org/keywords/sufficient-statistic","display_name":"Sufficient statistic","score":0.3546000123023987},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.3330000042915344},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.3257000148296356}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5789999961853027},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5670999884605408},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5422000288963318},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5085999965667725},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5063999891281128},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4819999933242798},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3822999894618988},{"id":"https://openalex.org/C178197554","wikidata":"https://www.wikidata.org/wiki/Q1099110","display_name":"Sufficient statistic","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34779998660087585},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.3257000148296356},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.3215000033378601},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3133000135421753},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31290000677108765},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C2781472820","wikidata":"https://www.wikidata.org/wiki/Q2154759","display_name":"Risk measure","level":3,"score":0.26820001006126404},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C44082924","wikidata":"https://www.wikidata.org/wiki/Q1767128","display_name":"Order statistic","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit63088.2025.11195446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2860730260","display_name":null,"funder_award_id":"ANR-22-PEFT-0010","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W575374134","https://openalex.org/W1484170624","https://openalex.org/W1585575029","https://openalex.org/W1587893424","https://openalex.org/W1634005169","https://openalex.org/W1647779468","https://openalex.org/W2019291268","https://openalex.org/W2087111615","https://openalex.org/W2099111195","https://openalex.org/W2134383396","https://openalex.org/W2981895326","https://openalex.org/W3149258821","https://openalex.org/W4206750164","https://openalex.org/W4251984466","https://openalex.org/W4252698487","https://openalex.org/W4285294221","https://openalex.org/W4289236186","https://openalex.org/W4406168614"],"related_works":[],"abstract_inverted_index":{"Inference":[0],"often":[1],"relies":[2],"on":[3],"compressed":[4],"data":[5,110],"due":[6],"to":[7,15,26,30,60],"communication,":[8],"storage,":[9],"or":[10],"privacy":[11],"constraints.":[12],"In":[13,84,118],"order":[14],"minimize":[16],"degradation":[17],"in":[18,57,64,91],"the":[19,31,41,44,48,51,89,102,119,140],"quality":[20],"of":[21,43,75,101,116,122],"inference,":[22],"it":[23],"is":[24,54],"desirable":[25],"tailor":[27],"compression":[28,35],"schemes":[29],"inference":[32,96],"task.":[33,49],"The":[34],"scheme":[36],"should":[37],"therefore":[38],"account":[39],"for":[40,47,95,108,135,148],"statistic":[42],"loss":[45,53,112,150],"relevant":[46],"While":[50],"expected":[52,149],"widely":[55],"considered,":[56],"applications":[58],"sensitive":[59],"large":[61],"losses\u2014such":[62],"as":[63,98],"safe":[65],"control":[66],"and":[67,114,129,139,151],"learning\u2014alternative":[68],"statistics":[69,78],"are":[70,79],"relevant.":[71],"A":[72],"key":[73],"family":[74],"these":[76],"alternative":[77],"obtained":[80],"via":[81],"risk":[82,92,136],"measures.":[83],"this":[85],"paper,":[86],"we":[87,125],"characterize":[88],"increase":[90],"measure":[93,137],"criteria":[94,138],"tasks":[97],"a":[99,130],"function":[100],"code":[103],"size.":[104],"Our":[105],"characterization":[106,134],"applies":[107],"general":[109],"statistics,":[111],"functions,":[113],"number":[115],"samples.":[117],"special":[120],"case":[121],"i.i.d.":[123],"data,":[124],"also":[126],"establish":[127],"asymptotics":[128],"connection":[131],"between":[132],"our":[133],"rate-distortion":[141],"function,":[142],"which":[143],"was":[144],"previously":[145],"only":[146],"known":[147],"excess":[152],"distortion":[153],"criteria.":[154]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-21T00:00:00"}
