{"id":"https://openalex.org/W7138000478","doi":"https://doi.org/10.1609/aaai.v40i29.39618","title":"Online Linear Regression with Paid Stochastic Features","display_name":"Online Linear Regression with Paid Stochastic Features","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138000478","doi":"https://doi.org/10.1609/aaai.v40i29.39618"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i29.39618","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39618","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i29.39618","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018784842","display_name":"Nadav Merlis","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nadav Merlis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129684158","display_name":"Kyoungseok Jang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kyoungseok Jang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129671078","display_name":"Nicol\u00f2 Cesa-Bianchi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicol\u00f2 Cesa-Bianchi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018784842"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17322835,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"29","first_page":"24370","last_page":"24378"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9559999704360962,"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":0.9559999704360962,"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":0.013700000010430813,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.005100000184029341,"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/regret","display_name":"Regret","score":0.7745000123977661},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5249000191688538},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.48829999566078186},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4691999852657318},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.462799996137619},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4399999976158142},{"id":"https://openalex.org/keywords/martingale","display_name":"Martingale (probability theory)","score":0.41609999537467957},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.41019999980926514}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7745000123977661},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5924000144004822},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5249000191688538},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.48829999566078186},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4691999852657318},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.462799996137619},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C48406656","wikidata":"https://www.wikidata.org/wiki/Q534112","display_name":"Martingale (probability theory)","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4049000144004822},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35690000653266907},{"id":"https://openalex.org/C206654554","wikidata":"https://www.wikidata.org/wiki/Q5374247","display_name":"Empirical measure","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3102000057697296},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.30320000648498535},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i29.39618","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39618","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:air.unimi.it:2434/1231215","is_oa":true,"landing_page_url":"https://hdl.handle.net/2434/1231215","pdf_url":null,"source":{"id":"https://openalex.org/S4306400516","display_name":"Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189158943","host_organization_name":"University of Milan","host_organization_lineage":["https://openalex.org/I189158943"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/bookPart"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i29.39618","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39618","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G13572568","display_name":null,"funder_award_id":"RS-2021-II211341","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"}],"funders":[{"id":"https://openalex.org/F4320321202","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98"},{"id":"https://openalex.org/F4320322252","display_name":"Israel Science Foundation","ror":"https://ror.org/04sazxf24"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,93,121],"study":[1,95],"an":[2],"online":[3],"linear":[4,79,168],"regression":[5],"setting":[6],"in":[7,98,114],"which":[8,99],"the":[9,18,24,74,78,88,96,100,115,128,136,142,155,161],"observed":[10],"feature":[11,61],"vectors":[12,62],"are":[13,63],"corrupted":[14],"by":[15],"noise":[16,25,105,129],"and":[17,91,104,109,133,167],"learner":[19],"can":[20,39,52],"pay":[21],"to":[22,55,119,160],"reduce":[23],"level.":[26],"In":[27],"practice,":[28],"this":[29],"may":[30],"happen":[31],"for":[32,35,164],"several":[33],"reasons:":[34],"example,":[36],"because":[37,49],"features":[38],"be":[40,53],"measured":[41],"more":[42,45],"accurately":[43],"using":[44],"expensive":[46],"equipment,":[47],"or":[48],"data":[50],"providers":[51],"incentivized":[54],"release":[56],"less":[57],"private":[58],"features.":[59],"Assuming":[60],"drawn":[64],"i.i.d.":[65],"from":[66],"a":[67,82],"fixed":[68],"but":[69],"unknown":[70,132],"distribution,":[71],"we":[72],"measure":[73],"learner's":[75],"regret":[76,112,137],"against":[77],"predictor":[80],"minimizing":[81],"notion":[83],"of":[84,144],"loss":[85,157],"that":[86,135,154],"combines":[87],"prediction":[89],"error":[90],"payment.":[92],"first":[94],"case":[97,143],"mapping":[101],"between":[102],"payments":[103,166],"covariance":[106,130],"is":[107,131,139],"known":[108,145],"prove":[110,134],"order-optimal":[111,124],"bounds":[113,125],"interaction":[116],"length":[117],"(up":[118],"log-factors).":[120],"then":[122],"derive":[123],"also":[126],"when":[127],"rate":[138],"worse":[140],"than":[141],"covariances.":[146],"Our":[147],"analysis":[148],"leverages":[149],"matrix":[150],"martingale":[151],"concentration,":[152],"showing":[153],"empirical":[156],"uniformly":[158],"converges":[159],"expected":[162],"one":[163],"all":[165],"predictors.":[169]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-18T00:00:00"}
