{"id":"https://openalex.org/W2922008860","doi":"https://doi.org/10.48550/arxiv.2605.02458","title":"Active multiple matrix completion with adaptive confidence sets","display_name":"Active multiple matrix completion with adaptive confidence sets","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W2922008860","doi":"https://doi.org/10.48550/arxiv.2605.02458","mag":"2922008860"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2605.02458","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.02458","pdf_url":"https://arxiv.org/pdf/2605.02458","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2605.02458","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021633683","display_name":"Andrea Locatelli","orcid":"https://orcid.org/0000-0001-7294-8309"},"institutions":[{"id":"https://openalex.org/I95793202","display_name":"Otto-von-Guericke-Universit\u00e4t Magdeburg","ror":"https://ror.org/00ggpsq73","country_code":"DE","type":"education","lineage":["https://openalex.org/I95793202"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Locatelli, Andrea","raw_affiliation_strings":["Otto-von-Guericke-Universit\u00e4t Magdeburg = Otto-von-Guericke University [Magdeburg]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Otto-von-Guericke-Universit\u00e4t Magdeburg = Otto-von-Guericke University [Magdeburg]","institution_ids":["https://openalex.org/I95793202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021658783","display_name":"Alexandra Carpentier","orcid":"https://orcid.org/0000-0002-1194-7385"},"institutions":[{"id":"https://openalex.org/I95793202","display_name":"Otto-von-Guericke-Universit\u00e4t Magdeburg","ror":"https://ror.org/00ggpsq73","country_code":"DE","type":"education","lineage":["https://openalex.org/I95793202"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Carpentier, Alexandra","raw_affiliation_strings":["Otto-von-Guericke-Universit\u00e4t Magdeburg = Otto-von-Guericke University [Magdeburg]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Otto-von-Guericke-Universit\u00e4t Magdeburg = Otto-von-Guericke University [Magdeburg]","institution_ids":["https://openalex.org/I95793202"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106038276","display_name":"Michal Valko","orcid":"https://orcid.org/0009-0007-8593-7765"},"institutions":[{"id":"https://openalex.org/I4210090411","display_name":"Google DeepMind (United Kingdom)","ror":"https://ror.org/00971b260","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210090411","https://openalex.org/I4210128969"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Valko, Michal","raw_affiliation_strings":["Sequential Learning","DeepMind [Paris]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sequential Learning","institution_ids":[]},{"raw_affiliation_string":"DeepMind [Paris]","institution_ids":["https://openalex.org/I4210090411"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9997000098228455,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9997000098228455,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.996999979019165,"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/T12288","display_name":"Optimization and Search Problems","score":0.9939000010490417,"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/matrix-completion","display_name":"Matrix completion","score":0.7964900732040405},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6682680249214172},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6645870804786682},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.6235470175743103},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5838831067085266},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.4815416932106018},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.4687374234199524},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.4607129991054535},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4576683044433594},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.44948962330818176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44365596771240234},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4201076626777649},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35137736797332764},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22095561027526855},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08629241585731506}],"concepts":[{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.7964900732040405},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682680249214172},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6645870804786682},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.6235470175743103},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5838831067085266},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.4815416932106018},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.4687374234199524},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.4607129991054535},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4576683044433594},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.44948962330818176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44365596771240234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4201076626777649},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35137736797332764},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22095561027526855},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08629241585731506},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:arXiv.org:2605.02458","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.02458","pdf_url":"https://arxiv.org/pdf/2605.02458","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-02387468v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-02387468","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"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":"International Conference on Artificial Intelligence and Statistics, 2019, Okinawa, Japan","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:lilloa.univ-lille.fr:20.500.12210/22142","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.12210/22142","pdf_url":null,"source":{"id":"https://openalex.org/S4306402203","display_name":"LillOA (Universit\u00e9 de Lille (University Of Lille))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210123514","host_organization_name":"Centre d'Etudes en Civilisations, Langues et Litt\u00e9ratures Etrang\u00e8res","host_organization_lineage":["https://openalex.org/I4210123514"],"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":"","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.2605.02458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02458","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2605.02458","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.02458","pdf_url":"https://arxiv.org/pdf/2605.02458","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1445013326","display_name":"Bayes on a Budget - big data and expensive models","funder_award_id":"ANR-16-CE23-0003","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G1729111207","display_name":"Extraction and Transfer of Knowledge in Reinforcement Learning","funder_award_id":"ANR-14-CE24-0010","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G3150829247","display_name":"GRK 2297: Mathematische Komplexit\u00e4tsreduktion","funder_award_id":"314838170","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G475298373","display_name":null,"funder_award_id":"GRK 2297 MathCoRe","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G5280056699","display_name":null,"funder_award_id":"GRK 2433","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G5328560043","display_name":null,"funder_award_id":"GRK 2297","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6846162920","display_name":null,"funder_award_id":"CRC 1294","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8777520260","display_name":null,"funder_award_id":"CE23-0003","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"},{"id":"https://openalex.org/F4320322727","display_name":"Minist\u00e8re de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","ror":"https://ror.org/03sjk9a61"},{"id":"https://openalex.org/F4320324478","display_name":"Fondation Math\u00e9matique Jacques Hadamard","ror":"https://ror.org/0153me927"},{"id":"https://openalex.org/F4320338463","display_name":"CHIST-ERA","ror":"https://ror.org/00rbzpz17"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2922008860.pdf","grobid_xml":"https://content.openalex.org/works/W2922008860.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2016058626","https://openalex.org/W3144354057","https://openalex.org/W4301205717","https://openalex.org/W4288107728","https://openalex.org/W2788210652","https://openalex.org/W2972830027","https://openalex.org/W3213035342","https://openalex.org/W2990709181","https://openalex.org/W3093503721","https://openalex.org/W2552353037"],"abstract_inverted_index":{"In":[0],"this":[1,67],"work,":[2],"we":[3],"formulate":[4],"a":[5,36,78,84,94,114],"new":[6,95],"multi-task":[7],"active":[8],"learning":[9],"setting":[10,68],"in":[11,66],"which":[12,32,87],"the":[13,27,53,62,73,104,108],"learner's":[14],"goal":[15],"is":[16,49,69,88,99,120],"to":[17,101,103],"solve":[18],"multiple":[19],"matrix":[20,33],"completion":[21],"problems":[22],"simultaneously.":[23],"At":[24],"each":[25,71],"round,":[26],"learner":[28],"can":[29,75],"choose":[30],"from":[31,38],"it":[34],"receives":[35],"sample":[37],"an":[39],"entry":[40],"drawn":[41],"uniformly":[42],"at":[43],"random.":[44],"Our":[45],"main":[46],"practical":[47],"motivation":[48],"market":[50],"segmentation,":[51],"where":[52],"matrices":[54,74],"represent":[55],"different":[56,59,79,85,109],"regions":[57],"with":[58,126],"preferences":[60],"of":[61,72,77,83,107],"customers.":[63],"The":[64],"challenge":[65],"that":[70,98,117],"be":[76],"size":[80],"and":[81,92,122],"also":[82],"rank":[86],"unknown.":[89],"We":[90,111],"provide":[91],"analyze":[93],"algorithm,":[96],"MAlocate":[97],"able":[100],"adapt":[102],"unknown":[105],"ranks":[106],"matrices.":[110],"then":[112],"give":[113],"lower-bound":[115],"showing":[116],"our":[118],"strategy":[119],"minimax-optimal":[121],"demonstrate":[123],"its":[124],"performance":[125],"synthetic":[127],"experiments.":[128]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2019-03-22T00:00:00"}
