{"id":"https://openalex.org/W4415366723","doi":"https://doi.org/10.1109/isit63088.2025.11195706","title":"A Markov Property of Empirical Distributions and the Performance of Compression-Based Denoisers","display_name":"A Markov Property of Empirical Distributions and the Performance of Compression-Based Denoisers","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4415366723","doi":"https://doi.org/10.1109/isit63088.2025.11195706"},"language":null,"primary_location":{"id":"doi:10.1109/isit63088.2025.11195706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195706","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/A5103101696","display_name":"Dan Song","orcid":"https://orcid.org/0000-0003-1594-7662"},"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":"Dan Song","raw_affiliation_strings":["Stanford University,Department of Electrical Engineering,Stanford,CA"],"affiliations":[{"raw_affiliation_string":"Stanford University,Department of Electrical Engineering,Stanford,CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003322209","display_name":"Ayfer \u00d6zg\u00fcr","orcid":"https://orcid.org/0000-0002-4455-4692"},"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":"Ayfer \u00d6zg\u00fcr","raw_affiliation_strings":["Stanford University,Department of Electrical Engineering,Stanford,CA"],"affiliations":[{"raw_affiliation_string":"Stanford University,Department of Electrical Engineering,Stanford,CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043344688","display_name":"Tsachy Weissman","orcid":"https://orcid.org/0009-0008-1099-691X"},"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,Department of Electrical Engineering,Stanford,CA"],"affiliations":[{"raw_affiliation_string":"Stanford University,Department of Electrical Engineering,Stanford,CA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103101696"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31243596,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.732699990272522,"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"}},"topics":[{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.732699990272522,"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/lossy-compression","display_name":"Lossy compression","score":0.565500020980835},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5145000219345093},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.48559999465942383},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4830000102519989},{"id":"https://openalex.org/keywords/markov-property","display_name":"Markov property","score":0.46470001339912415},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.46369999647140503},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.45649999380111694},{"id":"https://openalex.org/keywords/ergodic-theory","display_name":"Ergodic theory","score":0.44940000772476196},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4481000006198883},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44600000977516174}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6075000166893005},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5145000219345093},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.498199999332428},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.48559999465942383},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C189973286","wikidata":"https://www.wikidata.org/wiki/Q176695","display_name":"Markov property","level":4,"score":0.46470001339912415},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.46369999647140503},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.45649999380111694},{"id":"https://openalex.org/C122044880","wikidata":"https://www.wikidata.org/wiki/Q5498822","display_name":"Ergodic theory","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44600000977516174},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.4399999976158142},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.36800000071525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3675999939441681},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C73150493","wikidata":"https://www.wikidata.org/wiki/Q853922","display_name":"Hamming code","level":4,"score":0.35420000553131104},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.35199999809265137},{"id":"https://openalex.org/C2780841128","wikidata":"https://www.wikidata.org/wiki/Q5073781","display_name":"Characterization (materials science)","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C75438885","wikidata":"https://www.wikidata.org/wiki/Q3403615","display_name":"Large deviations theory","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33559998869895935},{"id":"https://openalex.org/C201779956","wikidata":"https://www.wikidata.org/wiki/Q5426803","display_name":"Ergodicity","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.32330000400543213},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C206654554","wikidata":"https://www.wikidata.org/wiki/Q5374247","display_name":"Empirical measure","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C162838799","wikidata":"https://www.wikidata.org/wiki/Q596077","display_name":"Counterexample","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.25049999356269836},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit63088.2025.11195706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit63088.2025.11195706","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1990742913","https://openalex.org/W2087284982","https://openalex.org/W2157721994","https://openalex.org/W2173680352","https://openalex.org/W3149258821","https://openalex.org/W4205855417","https://openalex.org/W4394863067"],"related_works":[],"abstract_inverted_index":{"We":[0,49],"consider":[1],"inferring":[2],"a":[3,16,37],"stationary":[4],"ergodic":[5],"source":[6,13],"by":[7,61],"lossily":[8],"compressing":[9],"observations":[10],"of":[11,24,29,45,78,87],"the":[12,22,25,46,64,68,73,84,102],"made":[14,95],"through":[15],"memoryless":[17,58],"noisy":[18],"channel.":[19],"By":[20],"bounding":[21],"deviation":[23],"empirical":[26],"joint":[27],"distribution":[28],"source,":[30],"observation,":[31],"and":[32,89],"inference":[33],"output":[34],"from":[35],"satisfying":[36],"Markov":[38],"property,":[39],"we":[40],"give":[41],"an":[42,97],"exact":[43],"characterization":[44],"loss":[47],"achieved.":[48],"show":[50],"that":[51],"this":[52],"analysis":[53],"is":[54,94],"applicable":[55],"to":[56,71,96],"general":[57],"noise":[59],"channels":[60],"deliberately":[62],"choosing":[63],"distortion":[65],"measure":[66],"for":[67],"lossy":[69],"compressor":[70],"match":[72],"channel":[74],"conditional":[75],"distribution.":[76],"Consequences":[77],"these":[79],"results":[80],"are":[81],"given":[82],"in":[83],"specific":[85],"cases":[86],"MSE":[88],"Hamming":[90],"loss.":[91],"A":[92],"comparison":[93],"indirect":[98],"rate-distortion":[99],"perspective":[100],"on":[101],"problem.":[103]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-21T00:00:00"}