{"id":"https://openalex.org/W4386057505","doi":"https://doi.org/10.1109/isit54713.2023.10206625","title":"Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling","display_name":"Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling","publication_year":2023,"publication_date":"2023-06-25","ids":{"openalex":"https://openalex.org/W4386057505","doi":"https://doi.org/10.1109/isit54713.2023.10206625"},"language":"en","primary_location":{"id":"doi:10.1109/isit54713.2023.10206625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit54713.2023.10206625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.repository.cam.ac.uk/bitstreams/74f1e9b6-def1-4633-96fc-4bd7c6490c6c/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092809254","display_name":"Pablo Pascual Cobo","orcid":"https://orcid.org/0000-0001-6791-8877"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Pablo Pascual Cobo","raw_affiliation_strings":["University of Cambridge,UK","University of Cambridge, UK"],"affiliations":[{"raw_affiliation_string":"University of Cambridge,UK","institution_ids":["https://openalex.org/I241749"]},{"raw_affiliation_string":"University of Cambridge, UK","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057708112","display_name":"Kuan Hsieh","orcid":"https://orcid.org/0000-0001-7500-9980"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuan Hsieh","raw_affiliation_strings":["Luminance Technologies Ltd,UK","Luminance Technologies Ltd, UK"],"affiliations":[{"raw_affiliation_string":"Luminance Technologies Ltd,UK","institution_ids":[]},{"raw_affiliation_string":"Luminance Technologies Ltd, UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081409970","display_name":"Ramji Venkataramanan","orcid":"https://orcid.org/0000-0001-7915-5432"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ramji Venkataramanan","raw_affiliation_strings":["University of Cambridge,UK","University of Cambridge, UK"],"affiliations":[{"raw_affiliation_string":"University of Cambridge,UK","institution_ids":["https://openalex.org/I241749"]},{"raw_affiliation_string":"University of Cambridge, UK","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5092809254"],"corresponding_institution_ids":["https://openalex.org/I241749"],"apc_list":null,"apc_paid":null,"fwci":0.6511,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.62871372,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"773","last_page":"778"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9919000267982483,"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/estimator","display_name":"Estimator","score":0.762406587600708},{"id":"https://openalex.org/keywords/generalized-linear-model","display_name":"Generalized linear model","score":0.6335840821266174},{"id":"https://openalex.org/keywords/minimum-mean-square-error","display_name":"Minimum mean square error","score":0.6059174537658691},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6023969650268555},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5892263650894165},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.521653950214386},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5112781524658203},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4673158526420593},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.4595983624458313},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.43705445528030396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4231388568878174},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.41203612089157104},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.40172746777534485},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.30769315361976624}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.762406587600708},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.6335840821266174},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.6059174537658691},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6023969650268555},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5892263650894165},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.521653950214386},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5112781524658203},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4673158526420593},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.4595983624458313},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.43705445528030396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4231388568878174},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.41203612089157104},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40172746777534485},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.30769315361976624},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/isit54713.2023.10206625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit54713.2023.10206625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},{"id":"pmh:oai:www.repository.cam.ac.uk:1810/372957","is_oa":true,"landing_page_url":"https://www.repository.cam.ac.uk/handle/1810/372957","pdf_url":"https://www.repository.cam.ac.uk/bitstreams/74f1e9b6-def1-4633-96fc-4bd7c6490c6c/download","source":{"id":"https://openalex.org/S4306401777","display_name":"Apollo (University of Cambridge)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I241749","host_organization_name":"University of Cambridge","host_organization_lineage":["https://openalex.org/I241749"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.17863/cam.111573","is_oa":true,"landing_page_url":"https://doi.org/10.17863/cam.111573","pdf_url":null,"source":{"id":"https://openalex.org/S7407050737","display_name":"Apollo","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:www.repository.cam.ac.uk:1810/372957","is_oa":true,"landing_page_url":"https://www.repository.cam.ac.uk/handle/1810/372957","pdf_url":"https://www.repository.cam.ac.uk/bitstreams/74f1e9b6-def1-4633-96fc-4bd7c6490c6c/download","source":{"id":"https://openalex.org/S4306401777","display_name":"Apollo (University of Cambridge)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I241749","host_organization_name":"University of Cambridge","host_organization_lineage":["https://openalex.org/I241749"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G584787399","display_name":null,"funder_award_id":"2436329","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386057505.pdf","grobid_xml":"https://content.openalex.org/works/W4386057505.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W1864365761","https://openalex.org/W1909826856","https://openalex.org/W1984830458","https://openalex.org/W2015911499","https://openalex.org/W2017687288","https://openalex.org/W2051275493","https://openalex.org/W2060430274","https://openalex.org/W2073868986","https://openalex.org/W2078397124","https://openalex.org/W2082029531","https://openalex.org/W2112038498","https://openalex.org/W2118473837","https://openalex.org/W2129857026","https://openalex.org/W2135046866","https://openalex.org/W2142949395","https://openalex.org/W2166670884","https://openalex.org/W2172679141","https://openalex.org/W2179644698","https://openalex.org/W2564702731","https://openalex.org/W2571114381","https://openalex.org/W2571527823","https://openalex.org/W2610971674","https://openalex.org/W2761315682","https://openalex.org/W2899900971","https://openalex.org/W2918745211","https://openalex.org/W2962714615","https://openalex.org/W2963072996","https://openalex.org/W2963519307","https://openalex.org/W2964003909","https://openalex.org/W2964130120","https://openalex.org/W2964227601","https://openalex.org/W2964282092","https://openalex.org/W3007447106","https://openalex.org/W3015834454","https://openalex.org/W3034356386","https://openalex.org/W3098848552","https://openalex.org/W3099126622","https://openalex.org/W3100706365","https://openalex.org/W3102437242","https://openalex.org/W3105600170","https://openalex.org/W3106678933","https://openalex.org/W3126400887","https://openalex.org/W3158248332","https://openalex.org/W3185757511","https://openalex.org/W4250955649","https://openalex.org/W4292482091","https://openalex.org/W4298263336","https://openalex.org/W4300263211","https://openalex.org/W4386057505","https://openalex.org/W6683110664","https://openalex.org/W6779672848","https://openalex.org/W6784429329"],"related_works":["https://openalex.org/W2075897667","https://openalex.org/W2907746047","https://openalex.org/W3125536267","https://openalex.org/W3081465059","https://openalex.org/W2274645452","https://openalex.org/W1510072949","https://openalex.org/W1969243170","https://openalex.org/W4242077822","https://openalex.org/W4386783521","https://openalex.org/W1556631438"],"abstract_inverted_index":{"We":[0,82],"consider":[1],"the":[2,32,55,58,61,104,113],"problem":[3],"of":[4,60,100,106],"signal":[5,122],"estimation":[6,20,92],"in":[7,18,46],"a":[8,51,97,107],"generalized":[9],"linear":[10,22],"model":[11],"(GLM).":[12],"GLMs":[13,39,75],"cover":[14],"many":[15,47],"canonical":[16],"problems":[17],"statistical":[19],"including":[21],"regression":[23],"and":[24,57,93],"phase":[25],"retrieval.":[26],"Recent":[27],"work":[28,68],"has":[29],"precisely":[30],"characterized":[31],"asymptotic":[33,114],"minimum":[34],"mean-squared":[35],"error":[36],"(MMSE)":[37],"for":[38,91,120],"with":[40,96,138],"i.i.d.":[41,133],"Gaussian":[42,134],"sensing":[43,80],"matrices.":[44,81],"However,":[45],"models":[48],"there":[49],"is":[50],"significant":[52],"gap":[53,72],"between":[54],"MMSE":[56],"performance":[59],"best":[62],"known":[63],"feasible":[64],"estimators.":[65],"In":[66],"this":[67,71],"we":[69],"address":[70],"by":[73],"considering":[74],"defined":[76],"via":[77],"spatially":[78,101,124],"coupled":[79,102,125],"propose":[83],"an":[84],"efficient":[85],"approximate":[86],"message":[87],"passing":[88],"(AMP)":[89],"algorithm":[90],"prove":[94],"that":[95,119],"simple":[98],"choice":[99],"design,":[103],"MSE":[105,131],"carefully":[108],"tuned":[109],"AMP":[110,139],"estimator":[111],"approaches":[112],"MMSE.":[115],"Numerical":[116],"results":[117],"show":[118],"finite":[121],"dimensions,":[123],"designs":[126,135],"can":[127],"yield":[128],"substantially":[129],"lower":[130],"than":[132],"when":[136],"used":[137],"algorithms.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}