{"id":"https://openalex.org/W3010665106","doi":"https://doi.org/10.1109/camsap45676.2019.9022450","title":"Recursive Shrinkage Covariance Learning in Adaptive Importance Sampling","display_name":"Recursive Shrinkage Covariance Learning in Adaptive Importance Sampling","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3010665106","doi":"https://doi.org/10.1109/camsap45676.2019.9022450","mag":"3010665106"},"language":"en","primary_location":{"id":"doi:10.1109/camsap45676.2019.9022450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap45676.2019.9022450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.research.ed.ac.uk/en/publications/9499add6-79fe-4d00-9415-b52d3ca496cb","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069588600","display_name":"Yousef El-Laham","orcid":"https://orcid.org/0000-0002-0728-737X"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yousef El-Laham","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085691944","display_name":"V\u0301\u0131ctor Elvira","orcid":"https://orcid.org/0000-0002-8967-4866"},"institutions":[{"id":"https://openalex.org/I4210133642","display_name":"IMT Nord Europe","ror":"https://ror.org/042rh9p26","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210133642"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Victor Elvira","raw_affiliation_strings":["IMT Lille Douai & CRIStAL laboratory, Villeneuve d'Ascq, France"],"affiliations":[{"raw_affiliation_string":"IMT Lille Douai & CRIStAL laboratory, Villeneuve d'Ascq, France","institution_ids":["https://openalex.org/I4210133642"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058778434","display_name":"M\u00f3nica F. Bugallo","orcid":"https://orcid.org/0000-0003-2963-1474"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monica Bugallo","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069588600"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.5772,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77239704,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2019","issue":null,"first_page":"624","last_page":"628"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9991000294685364,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.712241530418396},{"id":"https://openalex.org/keywords/rational-quadratic-covariance-function","display_name":"Rational quadratic covariance function","score":0.7090163826942444},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.6665281653404236},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.629061222076416},{"id":"https://openalex.org/keywords/mat\u00e9rn-covariance-function","display_name":"Mat\u00e9rn covariance function","score":0.614115834236145},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5037526488304138},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.5002613067626953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4948086142539978},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49081358313560486},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.49023252725601196},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4412012994289398},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2865845859050751}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.712241530418396},{"id":"https://openalex.org/C148893098","wikidata":"https://www.wikidata.org/wiki/Q7295778","display_name":"Rational quadratic covariance function","level":5,"score":0.7090163826942444},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.6665281653404236},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.629061222076416},{"id":"https://openalex.org/C118006245","wikidata":"https://www.wikidata.org/wiki/Q6792079","display_name":"Mat\u00e9rn covariance function","level":5,"score":0.614115834236145},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5037526488304138},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.5002613067626953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4948086142539978},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49081358313560486},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.49023252725601196},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4412012994289398},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2865845859050751}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/camsap45676.2019.9022450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap45676.2019.9022450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/9499add6-79fe-4d00-9415-b52d3ca496cb","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/9499add6-79fe-4d00-9415-b52d3ca496cb","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"El-Laham, Y, Elvira, V & Bugallo, M 2020, 'Recursive Shrinkage Covariance Learning in Adaptive Importance Sampling', Paper presented at 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 15/12/19 - 18/12/19. https://doi.org/10.1109/CAMSAP45676.2019.9022450","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.ed.ac.uk:publications/9499add6-79fe-4d00-9415-b52d3ca496cb","is_oa":false,"landing_page_url":"http://hdl.handle.net/20.500.11820/9499add6-79fe-4d00-9415-b52d3ca496cb","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"mag:3041723566","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002285886443071","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:openaire/9499add6-79fe-4d00-9415-b52d3ca496cb","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/9499add6-79fe-4d00-9415-b52d3ca496cb","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"El-Laham, Y, Elvira, V & Bugallo, M 2020, 'Recursive Shrinkage Covariance Learning in Adaptive Importance Sampling', Paper presented at 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 15/12/19 - 18/12/19. https://doi.org/10.1109/CAMSAP45676.2019.9022450","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W128740895","https://openalex.org/W1494925379","https://openalex.org/W1665662210","https://openalex.org/W1990512452","https://openalex.org/W1994616650","https://openalex.org/W1995780830","https://openalex.org/W2002409557","https://openalex.org/W2069739265","https://openalex.org/W2156840067","https://openalex.org/W2735102987","https://openalex.org/W2806458225","https://openalex.org/W3106340677","https://openalex.org/W3124158341","https://openalex.org/W3124691892","https://openalex.org/W4292403327"],"related_works":["https://openalex.org/W2022823194","https://openalex.org/W2126916073","https://openalex.org/W3211883524","https://openalex.org/W2109377650","https://openalex.org/W1987404909","https://openalex.org/W4386993326","https://openalex.org/W1627656821","https://openalex.org/W3025277714","https://openalex.org/W2018001152","https://openalex.org/W2050026105"],"abstract_inverted_index":{"The":[0,133],"estimation":[1],"of":[2,13,28,58,77,129,156,180],"covariance":[3,52,67,97,127,148,159],"matrices":[4,68],"has":[5,49],"been":[6],"a":[7,11,50,142,154],"central":[8],"problem":[9,85],"in":[10,69,88,105,141,174],"variety":[12],"disciplines,":[14],"including":[15],"quantitative":[16],"finance,":[17],"genomics,":[18],"and":[19],"signal":[20],"processing.":[21],"In":[22,115],"Bayesian":[23],"statistical":[24],"inference,":[25],"the":[26,43,55,74,89,94,111,126,130,146,165,175,178],"efficiency":[27],"Monte":[29],"Carlo":[30],"methods,":[31,172],"such":[32],"as":[33],"adaptive":[34],"importance":[35,90],"sampling":[36,91],"(AIS),":[37],"can":[38],"be":[39],"improved":[40],"significantly":[41],"if":[42],"distribution":[44,57],"used":[45],"to":[46,54,65,73,110],"draw":[47],"samples":[48,78,181],"similar":[51],"structure":[53,128],"posterior":[56],"interest.":[59],"Unfortunately,":[60],"it":[61],"is":[62,86,136,150,185],"generally":[63],"difficult":[64],"learn":[66],"high-dimensional":[70],"settings":[71,108],"due":[72,109],"large":[75],"number":[76,179],"needed":[79],"for":[80],"its":[81],"appropriate":[82],"estimation.":[83],"This":[84],"intensified":[87],"context,":[92],"where":[93,145,177],"usual":[95],"weighted":[96,158],"estimators":[98],"do":[99],"not":[100],"yield":[101],"full":[102],"rank":[103],"estimates":[104],"most":[106],"practical":[107],"weight":[112],"degeneracy":[113],"problem.":[114],"this":[116],"work,":[117],"we":[118],"propose":[119],"an":[120],"AIS":[121,171],"algorithm":[122],"that":[123,164],"robustly":[124],"learns":[125],"target":[131],"distribution.":[132],"new":[134],"method":[135,167],"based":[137],"on":[138],"applying":[139],"shrinkage":[140],"recursive":[143],"manner,":[144],"learned":[147],"matrix":[149],"constructed":[151],"iteratively":[152],"using":[153],"sequence":[155],"biased":[157],"estimators.":[160],"Simulation":[161],"results":[162],"indicate":[163],"proposed":[166],"outperforms":[168],"other":[169],"state-of-the-art":[170],"especially":[173],"case":[176],"drawn":[182],"per":[183],"iteration":[184],"relatively":[186],"small.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}