{"id":"https://openalex.org/W7139954582","doi":"https://doi.org/10.48550/arxiv.2603.18702","title":"Off-Policy Learning with Limited Supply","display_name":"Off-Policy Learning with Limited Supply","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139954582","doi":"https://doi.org/10.48550/arxiv.2603.18702"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18702","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.18702","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126314777","display_name":"Koichi Tanaka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tanaka, Koichi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130228686","display_name":"Ren Kishimoto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kishimoto, Ren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130220068","display_name":"Bushun Kawagishi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kawagishi, Bushun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102762244","display_name":"Yusuke Narita","orcid":"https://orcid.org/0000-0003-0314-3384"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Narita, Yusuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101782556","display_name":"Yasuo Yamamoto","orcid":"https://orcid.org/0009-0003-9257-9063"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yamamoto, Yasuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130253555","display_name":"Nobuyuki Shimizu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shimizu, Nobuyuki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130250423","display_name":"Yuta Saito","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saito, Yuta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.9043999910354614,"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":0.9043999910354614,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.010900000110268593,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10328","display_name":"Supply Chain and Inventory Management","score":0.010300000198185444,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5489000082015991},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.47760000824928284},{"id":"https://openalex.org/keywords/budget-constraint","display_name":"Budget constraint","score":0.42289999127388},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3991999924182892},{"id":"https://openalex.org/keywords/coupon","display_name":"Coupon","score":0.37130001187324524},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.35440000891685486},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.3393000066280365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7096999883651733},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5489000082015991},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3991999924182892},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3831999897956848},{"id":"https://openalex.org/C2779307704","wikidata":"https://www.wikidata.org/wiki/Q11794832","display_name":"Coupon","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3179999887943268},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.29510000348091125},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2863999903202057},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18702","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.18702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18702","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.42606815695762634,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"study":[1],"off-policy":[2],"learning":[3,171],"(OPL)":[4],"in":[5,13,29,46,115,121,156,226],"contextual":[6,30,227],"bandits,":[7],"which":[8],"plays":[9],"a":[10,14,37,108,131,166],"key":[11],"role":[12],"wide":[15],"range":[16],"of":[17,91,205],"real-world":[18,217],"applications":[19],"such":[20],"as":[21],"recommendation":[22],"systems":[23],"and":[24,53,147,216],"online":[25],"advertising.":[26],"Typical":[27],"OPL":[28,110,138,224],"bandits":[31],"assumes":[32],"an":[33],"unconstrained":[34,116],"environment":[35],"where":[36],"policy":[38,145],"can":[39],"select":[40],"the":[41,75,78,83,127,144,180,183,198],"same":[42],"item":[43,76,181],"infinitely.":[44],"However,":[45],"many":[47],"practical":[48],"applications,":[49],"including":[50],"coupon":[51],"allocation":[52,204],"e-commerce,":[54],"limited":[55,122,157,208,231],"supply":[56,123,158],"constrains":[57],"items":[58,190,206],"through":[59],"budget":[60],"limits":[61],"on":[62,68,161,189,213],"distributed":[63],"coupons":[64],"or":[65],"inventory":[66],"restrictions":[67],"products.":[69],"In":[70],"these":[71],"settings,":[72],"greedily":[73],"selecting":[74,179],"with":[77,151,172,182,191,207,230],"highest":[79,184],"expected":[80,105,185,194],"reward":[81],"for":[82,97],"current":[84],"user":[85],"may":[86,118,140],"lead":[87],"to":[88,142,197],"early":[89],"depletion":[90],"that":[92,112,135,149,220],"item,":[93],"making":[94],"it":[95],"unavailable":[96],"future":[98],"users":[99],"who":[100],"could":[101],"potentially":[102],"generate":[103],"higher":[104,193],"rewards.":[106],"As":[107],"result,":[109],"methods":[111,225],"are":[113],"optimal":[114],"settings":[117],"become":[119],"suboptimal":[120],"settings.":[124,159],"To":[125],"address":[126],"issue,":[128],"we":[129,164],"provide":[130],"theoretical":[132],"analysis":[133],"showing":[134],"conventional":[136],"greedy":[137],"approaches":[139],"fail":[141],"maximize":[143],"performance,":[146],"demonstrate":[148],"policies":[150],"superior":[152],"performance":[153],"must":[154],"exist":[155],"Based":[160],"this":[162],"insight,":[163],"introduce":[165],"novel":[167],"method":[168],"called":[169],"Off-Policy":[170],"Limited":[173],"Supply":[174],"(OPLS).":[175],"Rather":[176],"than":[177],"simply":[178],"reward,":[186],"OPLS":[187,221],"focuses":[188],"relatively":[192],"rewards":[195],"compared":[196],"other":[199],"users,":[200],"enabling":[201],"more":[202],"efficient":[203],"supply.":[209,232],"Our":[210],"empirical":[211],"results":[212],"both":[214],"synthetic":[215],"datasets":[218],"show":[219],"outperforms":[222],"existing":[223],"bandit":[228],"problems":[229]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}