{"id":"https://openalex.org/W7156264107","doi":"https://doi.org/10.48550/arxiv.2604.22140","title":"Concave Statistical Utility Maximization Bandits via Influence-Function Gradients","display_name":"Concave Statistical Utility Maximization Bandits via Influence-Function Gradients","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7156264107","doi":"https://doi.org/10.48550/arxiv.2604.22140"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22140","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22140","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.22140","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134683545","display_name":"Mat\u00edas Carrasco","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Carrasco, Mat\u00edas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070470540","display_name":"Alejandro Cholaquidis","orcid":"https://orcid.org/0000-0001-9126-1860"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cholaquidis, Alejandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5134683545"],"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":0.9681000113487244,"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":0.9681000113487244,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.01590000092983246,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.003700000001117587,"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":0.7457000017166138},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.652400016784668},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.5299999713897705},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5160999894142151},{"id":"https://openalex.org/keywords/concave-function","display_name":"Concave function","score":0.5139999985694885},{"id":"https://openalex.org/keywords/simplex","display_name":"Simplex","score":0.5090000033378601},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.46889999508857727},{"id":"https://openalex.org/keywords/expected-utility-hypothesis","display_name":"Expected utility hypothesis","score":0.3467999994754791}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7457000017166138},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.652400016784668},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6223999857902527},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5572999715805054},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.5299999713897705},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C66690126","wikidata":"https://www.wikidata.org/wiki/Q2914302","display_name":"Concave function","level":3,"score":0.5139999985694885},{"id":"https://openalex.org/C62438384","wikidata":"https://www.wikidata.org/wiki/Q331350","display_name":"Simplex","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.40230000019073486},{"id":"https://openalex.org/C205706631","wikidata":"https://www.wikidata.org/wiki/Q2319304","display_name":"Expected utility hypothesis","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C144521790","wikidata":"https://www.wikidata.org/wiki/Q134164","display_name":"Simplex algorithm","level":3,"score":0.3190000057220459},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C2985793214","wikidata":"https://www.wikidata.org/wiki/Q3274096","display_name":"Utility maximization","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.27799999713897705},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.2741999924182892},{"id":"https://openalex.org/C194387892","wikidata":"https://www.wikidata.org/wiki/Q1747770","display_name":"Stochastic optimization","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C2779044140","wikidata":"https://www.wikidata.org/wiki/Q3274096","display_name":"Utility maximization problem","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22140","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22140","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":"doi:10.48550/arxiv.2604.22140","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22140","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.43254026770591736}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,100],"study":[1],"stochastic":[2,72],"multi-armed":[3],"bandits":[4],"in":[5],"which":[6],"the":[7,14,30,45,57,97,106,111,116],"objective":[8],"is":[9,54,121],"a":[10,48,86],"statistical":[11,64],"functional":[12],"of":[13,96],"long-run":[15],"reward":[16,21],"distribution,":[17],"rather":[18],"than":[19],"expected":[20],"alone.":[22],"Under":[23],"mild":[24],"continuity":[25],"assumptions,":[26],"we":[27,66],"show":[28],"that":[29,104],"infinite-horizon":[31],"problem":[32],"reduces":[33],"to":[34,70,80],"optimizing":[35],"over":[36],"stationary":[37],"mixed":[38],"policies:":[39],"each":[40],"weight":[41],"vector":[42],"\\(w\\)":[43],"on":[44,85],"simplex":[46],"induces":[47],"mixture":[49],"law":[50],"\\(P^w\\),":[51],"and":[52,93,128,132,140],"performance":[53],"measured":[55],"by":[56,114],"concave":[58,125],"utility":[59],"\\(U(w)=\\mathfrak":[60],"U(P^w)\\).":[61],"For":[62],"differentiable":[63],"utilities,":[65],"use":[67],"influence-function":[68,142],"calculus":[69],"derive":[71],"gradient":[73],"estimators":[74],"from":[75,110],"bandit":[76],"feedback.":[77],"This":[78],"leads":[79],"an":[81],"entropic":[82],"mirror-ascent":[83,107],"algorithm":[84],"truncated":[87],"simplex,":[88],"implemented":[89],"through":[90,130],"multiplicative-weights":[91],"updates":[92],"plug-in":[94,141],"estimates":[95],"influence":[98,117],"function.":[99,118],"establish":[101],"regret":[102],"bounds":[103],"separate":[105],"optimization":[108],"error":[109],"bias":[112],"caused":[113],"estimating":[115],"The":[119],"framework":[120],"developed":[122],"for":[123],"general":[124],"distributional":[126],"utilities":[127],"illustrated":[129],"variance":[131],"Wasserstein":[133],"objectives,":[134],"with":[135],"numerical":[136],"experiments":[137],"comparing":[138],"exact":[139],"implementations.":[143]},"counts_by_year":[],"updated_date":"2026-04-28T06:12:00.211691","created_date":"2026-04-28T00:00:00"}