{"id":"https://openalex.org/W7155049082","doi":"https://doi.org/10.48550/arxiv.2604.18143","title":"Distributional Off-Policy Evaluation with Deep Quantile Process Regression","display_name":"Distributional Off-Policy Evaluation with Deep Quantile Process Regression","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155049082","doi":"https://doi.org/10.48550/arxiv.2604.18143"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18143","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18143","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.2604.18143","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134122920","display_name":"Qi Kuang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuang, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134207617","display_name":"Chao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067802381","display_name":"Yuling Jiao","orcid":"https://orcid.org/0000-0002-2159-494X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiao, Yuling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134171935","display_name":"Fan Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Fan","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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.23340000212192535,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.23340000212192535,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10791","display_name":"Advanced Control Systems Optimization","score":0.07620000094175339,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10357","display_name":"Efficiency Analysis Using DEA","score":0.061900001019239426,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.8808000087738037},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.7473000288009644},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.45339998602867126},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4348999857902527},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.3982999920845032},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.3917999863624573},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3781999945640564}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.8808000087738037},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.7473000288009644},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5874000191688538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48190000653266907},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35089999437332153},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3280999958515167},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.32019999623298645},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30149999260902405},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18143","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18143","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.2604.18143","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18143","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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5799000263214111}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"the":[3,17,20,33,70,96,124,129,163],"off-policy":[4],"evaluation":[5],"(OPE)":[6],"problem":[7],"from":[8],"a":[9,42,53,85,136],"distributional":[10,102,169],"perspective.":[11],"Rather":[12],"than":[13,158],"focusing":[14],"solely":[15],"on":[16],"expectation":[18],"of":[19,92,168],"total":[21],"return,":[22],"as":[23],"in":[24],"most":[25],"existing":[26,77],"OPE":[27,46,103],"methods,":[28,160],"we":[29,40],"aim":[30],"to":[31,83,134],"estimate":[32,80,84,135],"entire":[34],"return":[35,126],"distribution.":[36],"To":[37],"this":[38],"end,":[39],"introduce":[41],"quantile-based":[43],"approach":[44],"for":[45,101],"using":[47,128,140],"deep":[48,71,105],"quantile":[49,72,87],"process":[50,73],"regression,":[51],"presenting":[52],"novel":[54],"algorithm":[55],"called":[56],"Deep":[57],"Quantile":[58],"Process":[59],"regression-based":[60],"Off-Policy":[61],"Evaluation":[62],"(DQPOPE).":[63],"We":[64,115],"provide":[65],"new":[66],"theoretical":[67,109],"insights":[68],"into":[69],"regression":[74],"technique,":[75],"extending":[76],"approaches":[78],"that":[79,117,147],"discrete":[81],"quantiles":[82],"continuous":[86],"function.":[88],"A":[89],"key":[90],"contribution":[91],"our":[93],"work":[94],"is":[95],"rigorous":[97],"sample":[98,131],"complexity":[99],"analysis":[100,110],"with":[104,111],"neural":[106],"networks,":[107],"bridging":[108],"practical":[112,164],"algorithmic":[113],"implementations.":[114],"show":[116,146],"DQPOPE":[118,148],"achieves":[119],"statistical":[120],"advantages":[121],"by":[122],"estimating":[123],"full":[125],"distribution":[127],"same":[130],"size":[132],"required":[133],"single":[137],"policy":[138,155],"value":[139,156],"conventional":[141],"methods.":[142],"Empirical":[143],"studies":[144],"further":[145],"provides":[149],"significantly":[150],"more":[151],"precise":[152],"and":[153,166],"robust":[154],"estimates":[157],"standard":[159],"thereby":[161],"enhancing":[162],"applicability":[165],"effectiveness":[167],"reinforcement":[170],"learning":[171],"approaches.":[172]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-22T00:00:00"}