{"id":"https://openalex.org/W4225509525","doi":"https://doi.org/10.48550/arxiv.2203.10124","title":"Approximate Function Evaluation via Multi-Armed Bandits","display_name":"Approximate Function Evaluation via Multi-Armed Bandits","publication_year":2022,"publication_date":"2022-03-18","ids":{"openalex":"https://openalex.org/W4225509525","doi":"https://doi.org/10.48550/arxiv.2203.10124"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2203.10124","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.10124","pdf_url":"https://arxiv.org/pdf/2203.10124","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":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.10124","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055644525","display_name":"Tavor Z. Baharav","orcid":"https://orcid.org/0000-0001-8924-0243"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Baharav, Tavor Z.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051321761","display_name":"Gary J. Cheng","orcid":"https://orcid.org/0000-0002-1184-2946"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Gary","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001436196","display_name":"Mert Pilanc\u0131","orcid":"https://orcid.org/0000-0002-0870-9992"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pilanci, Mert","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024072566","display_name":"David Tse","orcid":"https://orcid.org/0000-0003-1460-5900"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tse, David","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055644525"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9940000176429749,"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":0.9940000176429749,"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/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9656999707221985,"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/function","display_name":"Function (biology)","score":0.6316659450531006},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6163379549980164},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5776770710945129},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.5307068228721619},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.5265486836433411},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5248199105262756},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4931604266166687},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4319967031478882},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42427361011505127},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.41660284996032715},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3460950553417206},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.3377148509025574},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.26852741837501526},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2604328393936157},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11995089054107666},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.11231723427772522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.06907826662063599}],"concepts":[{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.6316659450531006},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6163379549980164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5776770710945129},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.5307068228721619},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.5265486836433411},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5248199105262756},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4931604266166687},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4319967031478882},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42427361011505127},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.41660284996032715},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3460950553417206},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.3377148509025574},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.26852741837501526},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2604328393936157},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11995089054107666},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.11231723427772522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.06907826662063599},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2203.10124","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.10124","pdf_url":"https://arxiv.org/pdf/2203.10124","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":"","raw_type":null},{"id":"doi:10.48550/arxiv.2203.10124","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2203.10124","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2203.10124","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.10124","pdf_url":"https://arxiv.org/pdf/2203.10124","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":"","raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1676609285","https://openalex.org/W3148934225","https://openalex.org/W2351648145","https://openalex.org/W2038165226","https://openalex.org/W2052845382","https://openalex.org/W4254549582","https://openalex.org/W2148483050","https://openalex.org/W3124109721","https://openalex.org/W2916152223","https://openalex.org/W2042102171"],"abstract_inverted_index":{"We":[0,63,92,109],"study":[1],"the":[2,6,42,56,74,118],"problem":[3],"of":[4,8,36,41,76,90],"estimating":[5],"value":[7],"a":[9,29],"known":[10],"smooth":[11],"function":[12,43],"$f$":[13,106],"at":[14,82],"an":[15,65,86],"unknown":[16],"point":[17],"$\\boldsymbol\u03bc":[18],"\\in":[19],"\\mathbb{R}^n$,":[20],"where":[21],"each":[22,77],"component":[23],"$\u03bc_i$":[24],"can":[25],"be":[26],"sampled":[27],"via":[28],"noisy":[30],"oracle.":[31],"Sampling":[32],"more":[33,49],"frequently":[34],"components":[35],"$\\boldsymbol\u03bc$":[37,53],"corresponding":[38],"to":[39,70,73,96,98],"directions":[40],"with":[44,80,114],"larger":[45],"directional":[46],"derivatives":[47],"is":[48,54,107],"sample-efficient.":[50],"However,":[51],"as":[52],"unknown,":[55],"optimal":[57],"sampling":[58],"frequencies":[59],"are":[60],"also":[61],"unknown.":[62],"design":[64],"instance-adaptive":[66],"algorithm":[67,95],"that":[68],"learns":[69],"sample":[71],"according":[72],"importance":[75],"coordinate,":[78],"and":[79,101],"probability":[81],"least":[83],"$1-\u03b4$":[84],"returns":[85],"$\u03b5$":[87],"accurate":[88],"estimate":[89],"$f(\\boldsymbol\u03bc)$.":[91],"generalize":[93],"our":[94,111],"adapt":[97],"heteroskedastic":[99],"noise,":[100],"prove":[102],"asymptotic":[103],"optimality":[104],"when":[105],"linear.":[108],"corroborate":[110],"theoretical":[112],"results":[113],"numerical":[115],"experiments,":[116],"showing":[117],"dramatic":[119],"gains":[120],"afforded":[121],"by":[122],"adaptivity.":[123]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
