{"id":"https://openalex.org/W4312763325","doi":"https://doi.org/10.1109/mlsp55214.2022.9943319","title":"High Dimensional Stochastic Linear Contextual Bandit with Missing Covariates","display_name":"High Dimensional Stochastic Linear Contextual Bandit with Missing Covariates","publication_year":2022,"publication_date":"2022-08-22","ids":{"openalex":"https://openalex.org/W4312763325","doi":"https://doi.org/10.1109/mlsp55214.2022.9943319"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp55214.2022.9943319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55214.2022.9943319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5007221235","display_name":"Byoungwook Jang","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Byoungwook Jang","raw_affiliation_strings":["University of Michigan,Department of Statistics","Department of Statistics, University of Michigan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan,Department of Statistics","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"Department of Statistics, University of Michigan","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036170326","display_name":"Julia F. Nepper","orcid":"https://orcid.org/0000-0002-0391-6394"},"institutions":[{"id":"https://openalex.org/I2802999273","display_name":"Wisconsin Institutes for Discovery","ror":"https://ror.org/05gbven85","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802999273"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Julia Nepper","raw_affiliation_strings":["Wisconsin Institute for Discovery"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wisconsin Institute for Discovery","institution_ids":["https://openalex.org/I2802999273"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079313598","display_name":"Marc G. Chevrette","orcid":"https://orcid.org/0000-0002-7209-0717"},"institutions":[{"id":"https://openalex.org/I2802999273","display_name":"Wisconsin Institutes for Discovery","ror":"https://ror.org/05gbven85","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802999273"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marc Chevrette","raw_affiliation_strings":["Wisconsin Institute for Discovery"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wisconsin Institute for Discovery","institution_ids":["https://openalex.org/I2802999273"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060743111","display_name":"Jo Handelsman","orcid":"https://orcid.org/0000-0003-3488-5030"},"institutions":[{"id":"https://openalex.org/I2802999273","display_name":"Wisconsin Institutes for Discovery","ror":"https://ror.org/05gbven85","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802999273"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jo Handelsman","raw_affiliation_strings":["Wisconsin Institute for Discovery"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wisconsin Institute for Discovery","institution_ids":["https://openalex.org/I2802999273"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077692655","display_name":"Alfred O. Hero","orcid":"https://orcid.org/0000-0002-2531-9670"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alfred O. Hero","raw_affiliation_strings":["University of Michigan,Department of Statistics","Department of EECS, University of Michigan","Department of Statistics, University of Michigan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan,Department of Statistics","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"Department of EECS, University of Michigan","institution_ids":["https://openalex.org/I27837315"]},{"raw_affiliation_string":"Department of Statistics, University of Michigan","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19700269,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9904999732971191,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/covariate","display_name":"Covariate","score":0.744990348815918},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.7434089183807373},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6556099653244019},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5915336012840271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5196400880813599},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.46449047327041626},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42905062437057495},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.42888128757476807},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42439520359039307},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3671220541000366},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35958331823349},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2667310833930969}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.744990348815918},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7434089183807373},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6556099653244019},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5915336012840271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5196400880813599},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.46449047327041626},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42905062437057495},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.42888128757476807},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42439520359039307},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3671220541000366},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35958331823349},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2667310833930969},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp55214.2022.9943319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55214.2022.9943319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6100000143051147,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1515782956","https://openalex.org/W2077870633","https://openalex.org/W2092058109","https://openalex.org/W2099210013","https://openalex.org/W2114091522","https://openalex.org/W2206616202","https://openalex.org/W2563145001","https://openalex.org/W2616050959","https://openalex.org/W2734936460","https://openalex.org/W2750928421","https://openalex.org/W2904599719","https://openalex.org/W2965489736","https://openalex.org/W3100937728","https://openalex.org/W3103380172","https://openalex.org/W3124121179","https://openalex.org/W3169949848","https://openalex.org/W6677198402","https://openalex.org/W6680344438","https://openalex.org/W6766076519","https://openalex.org/W6780573391"],"related_works":["https://openalex.org/W2971351794","https://openalex.org/W4376155396","https://openalex.org/W1947085858","https://openalex.org/W2174986909","https://openalex.org/W2527791220","https://openalex.org/W3032945164","https://openalex.org/W2057612738","https://openalex.org/W128985311","https://openalex.org/W2965517341","https://openalex.org/W2952500852"],"abstract_inverted_index":{"Recent":[0],"works":[1],"in":[2,9,86,118],"bandit":[3,69],"problems":[4],"adopted":[5],"lasso":[6,29],"convergence":[7,30],"theory":[8],"the":[10,25,34,46,49,53,59,79,83,94,112,119,127],"sequential":[11,140],"decision-making":[12],"setting.":[13],"Even":[14],"with":[15],"fully":[16],"observed":[17],"contexts,":[18],"there":[19],"are":[20],"technical":[21],"challenges":[22],"that":[23,93],"hinder":[24],"application":[26,129],"of":[27,61,88,115,130,142],"existing":[28],"theory:":[31],"1)":[32],"proving":[33],"restricted":[35],"eigenvalue":[36],"condition":[37],"under":[38],"conditionally":[39],"sub-Gaussian":[40],"noise":[41],"and":[42,52],"2)":[43],"accounting":[44],"for":[45,66,126,133],"dependence":[47],"between":[48],"context":[50,120],"variables":[51],"chosen":[54],"actions.":[55],"This":[56],"paper":[57],"studies":[58],"effect":[60],"missing":[62],"covariates":[63,117],"on":[64,78],"regret":[65,80,95],"stochastic":[67],"linear":[68],"algorithms.":[70],"Our":[71],"work":[72],"provides":[73],"a":[74,139],"high-probability":[75],"upper":[76],"bound":[77],"incurred":[81],"by":[82,100,138],"proposed":[84],"algorithm":[85,125],"terms":[87],"covariate":[89],"sampling":[90],"probabilities,":[91],"showing":[92],"degrades":[96],"due":[97],"to":[98],"missingness":[99],"at":[101],"most":[102],"<tex":[103,108],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[104,109],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\zeta_{min}^{2}$</tex>":[105],",":[106],"where":[107],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\zeta_{min}$</tex>":[110],"is":[111],"minimum":[113],"probability":[114],"observing":[116],"vector.":[121],"We":[122],"illustrate":[123],"our":[124],"practical":[128],"experimental":[131],"design":[132],"collecting":[134],"gene":[135],"expression":[136],"data":[137],"selection":[141],"class":[143],"discriminating":[144],"DNA":[145],"probes.":[146]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
