{"id":"https://openalex.org/W4414538479","doi":"https://doi.org/10.1109/icc52391.2025.11161439","title":"Data-Driven Neural Estimation of Indirect Rate-Distortion Function","display_name":"Data-Driven Neural Estimation of Indirect Rate-Distortion Function","publication_year":2025,"publication_date":"2025-06-08","ids":{"openalex":"https://openalex.org/W4414538479","doi":"https://doi.org/10.1109/icc52391.2025.11161439"},"language":"en","primary_location":{"id":"doi:10.1109/icc52391.2025.11161439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","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/A5112840113","display_name":"Zichao Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zichao Yu","raw_affiliation_strings":["University of Science and Technology of China,Department of Electronic Engineering and Information Science,Hefei,China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China,Department of Electronic Engineering and Information Science,Hefei,China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044703198","display_name":"Sun Qiang","orcid":"https://orcid.org/0000-0002-0374-3286"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Sun","raw_affiliation_strings":["University of Science and Technology of China,Department of Electronic Engineering and Information Science,Hefei,China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China,Department of Electronic Engineering and Information Science,Hefei,China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100360013","display_name":"Wenyi Zhang","orcid":"https://orcid.org/0009-0004-5175-4993"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyi Zhang","raw_affiliation_strings":["University of Science and Technology of China,Department of Electronic Engineering and Information Science,Hefei,China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China,Department of Electronic Engineering and Information Science,Hefei,China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112840113"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37826031,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2827","last_page":"2832"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10638","display_name":"Optical measurement and interference techniques","score":0.9550999999046326,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.6183000206947327},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.5544000267982483},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5388000011444092},{"id":"https://openalex.org/keywords/distortion-function","display_name":"Distortion function","score":0.5335000157356262},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.4952000081539154},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.4609000086784363},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44699999690055847},{"id":"https://openalex.org/keywords/conditional-expectation","display_name":"Conditional expectation","score":0.43369999527931213},{"id":"https://openalex.org/keywords/probability-measure","display_name":"Probability measure","score":0.4269999861717224}],"concepts":[{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6183000206947327},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.548799991607666},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5388000011444092},{"id":"https://openalex.org/C2780803321","wikidata":"https://www.wikidata.org/wiki/Q5283073","display_name":"Distortion function","level":3,"score":0.5335000157356262},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.4952000081539154},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.4609000086784363},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.45750001072883606},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C21031990","wikidata":"https://www.wikidata.org/wiki/Q355020","display_name":"Probability measure","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41269999742507935},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4025999903678894},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36239999532699585},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.33250001072883606},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.31540000438690186},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C166437778","wikidata":"https://www.wikidata.org/wiki/Q50695","display_name":"Quadratic function","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.2535000145435333},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C178197554","wikidata":"https://www.wikidata.org/wiki/Q1099110","display_name":"Sufficient statistic","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc52391.2025.11161439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3543960157","display_name":null,"funder_award_id":"62231022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1570963478","https://openalex.org/W1619671289","https://openalex.org/W1792373373","https://openalex.org/W1990742913","https://openalex.org/W2068143054","https://openalex.org/W2076092328","https://openalex.org/W2080570118","https://openalex.org/W2099186079","https://openalex.org/W2114085948","https://openalex.org/W2121427778","https://openalex.org/W2135469284","https://openalex.org/W2137983211","https://openalex.org/W2148986322","https://openalex.org/W2173267959","https://openalex.org/W2522673337","https://openalex.org/W2801692050","https://openalex.org/W2803363575","https://openalex.org/W2964019610","https://openalex.org/W3022414928","https://openalex.org/W3149258821","https://openalex.org/W3197256000","https://openalex.org/W4226483158","https://openalex.org/W4302366875","https://openalex.org/W4318149039","https://openalex.org/W4360898393","https://openalex.org/W4376479271","https://openalex.org/W4386066575","https://openalex.org/W4406168614","https://openalex.org/W4408083596"],"related_works":[],"abstract_inverted_index":{"The":[0,57],"rate-distortion":[1,19],"function":[2,20],"(RDF)":[3],"has":[4],"long":[5],"been":[6],"an":[7,32],"information-theoretic":[8],"benchmark":[9],"for":[10,48],"data":[11],"compression.":[12],"As":[13],"its":[14,72,110],"natural":[15],"extension,":[16],"the":[17,24,27,36,40,67,77,92,102,107,112,124,132,148,153,156,177,185,189,201,211,218,223],"indirect":[18],"(iRDF)":[21],"corresponds":[22],"to":[23,83,90,175,184],"scenario":[25,44],"where":[26],"encoder":[28],"can":[29,59,165],"only":[30],"access":[31],"observation":[33],"correlated":[34],"with":[35,66],"source,":[37],"rather":[38],"than":[39],"source":[41,108],"itself.":[42],"Such":[43],"is":[45,131],"also":[46],"relevant":[47],"modern":[49],"applications":[50],"like":[51],"remote":[52],"sensing":[53],"and":[54,109,137,155,192,213,220],"goal-oriented":[55],"communication.":[56],"iRDF":[58,93,154,178,190],"be":[60,116,166],"reduced":[61,149],"into":[62],"a":[63,84,139,144,159,180,194],"standard":[64],"RDF":[65],"distortion":[68,150,203],"measure":[69,151,204],"replaced":[70],"by":[71,169],"conditional":[73,113,129],"expectation":[74,114,130],"conditioned":[75],"upon":[76],"observation.":[78],"This":[79],"reduction,":[80],"however,":[81],"leads":[82],"nontrivial":[85],"challenge":[86],"when":[87],"one":[88],"needs":[89],"estimate":[91],"given":[94],"datasets":[95],"only,":[96],"because":[97],"without":[98],"statistical":[99],"knowledge":[100],"of":[101,158,188,210,222],"joint":[103],"probability":[104],"distribution":[105],"between":[106,147],"observation,":[111],"cannot":[115],"evaluated.":[117],"To":[118],"tackle":[119],"this":[120],"challenge,":[121],"starting":[122],"from":[123],"well":[125],"known":[126],"fact":[127],"that":[128],"minimum":[133],"mean-squared":[134],"error":[135],"estimator":[136],"exploiting":[138],"Markovian":[140],"relationship,":[141],"we":[142],"identify":[143],"functional":[145],"equivalence":[146],"in":[152],"solution":[157],"quadratic":[160],"loss":[161],"minimization":[162],"problem,":[163],"which":[164],"efficiently":[167],"approximated":[168],"neural":[170,195],"network":[171,196],"approach.":[172],"We":[173],"proceed":[174],"reformulate":[176],"as":[179],"variational":[181],"problem":[182],"corresponding":[183],"Lagrangian":[186],"representation":[187],"curve,":[191],"propose":[193],"based":[197],"approximate":[198],"solution,":[199,212],"integrating":[200],"aforementioned":[202],"estimator.":[205],"Asymptotic":[206],"analysis":[207],"guarantees":[208],"consistency":[209],"numerical":[214],"experimental":[215],"results":[216],"demonstrate":[217],"accuracy":[219],"effectiveness":[221],"algorithm.":[224]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}