{"id":"https://openalex.org/W7141740085","doi":"https://doi.org/10.48550/arxiv.2603.24704","title":"Conformal Selective Prediction with General Risk Control","display_name":"Conformal Selective Prediction with General Risk Control","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7141740085","doi":"https://doi.org/10.48550/arxiv.2603.24704"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24704","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24704","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":"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.2603.24704","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130798576","display_name":"Tian Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130742220","display_name":"Ying Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Ying","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2637999951839447,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2637999951839447,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.20000000298023224,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.10639999806880951,"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/bounded-function","display_name":"Bounded function","score":0.5472999811172485},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5360000133514404},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.49239999055862427},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.48649999499320984},{"id":"https://openalex.org/keywords/risk-management","display_name":"Risk management","score":0.43059998750686646},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4302000105381012},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4284999966621399},{"id":"https://openalex.org/keywords/conformal-map","display_name":"Conformal map","score":0.38269999623298645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5555999875068665},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5360000133514404},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C98214594","wikidata":"https://www.wikidata.org/wiki/Q850275","display_name":"Conformal map","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.373199999332428},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30880001187324524},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2939999997615814},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2888999879360199},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28600001335144043},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.265500009059906},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C27504089","wikidata":"https://www.wikidata.org/wiki/Q2602080","display_name":"Audit risk","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24704","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24704","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":"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.2603.24704","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24704","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":"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"deploying":[1],"artificial":[2],"intelligence":[3],"(AI)":[4],"models,":[5],"selective":[6],"prediction":[7,16],"offers":[8,71],"the":[9,39,77,84,89,115,147,157,174],"option":[10],"to":[11,29,87,141,167,185],"abstain":[12],"from":[13],"making":[14],"a":[15,52,102,125],"when":[17],"uncertain":[18],"about":[19],"model":[20,40,62],"quality.":[21],"To":[22],"fulfill":[23],"its":[24],"promise,":[25],"it":[26],"is":[27,41,127],"crucial":[28],"enforce":[30],"strict":[31],"and":[32,63,67,95,162,179,194],"precise":[33],"error":[34,153,186],"control":[35,48],"over":[36],"cases":[37,81],"where":[38],"trusted.":[42],"We":[43,172],"propose":[44],"Selective":[45],"Conformal":[46],"Risk":[47],"with":[49,114,151,169,177],"E-values":[50],"(SCoRE),":[51],"new":[53],"framework":[54],"for":[55,59],"deriving":[56],"such":[57],"decisions":[58,150],"any":[60,64,134],"trained":[61],"user-defined,":[65],"bounded":[66],"continuously-valued":[68],"risk.":[69],"SCoRE":[70,99,155],"two":[72],"types":[73],"of":[74,104,159],"guarantees":[75],"on":[76,140],"risk":[78,117,192],"among":[79],"``positive''":[80],"in":[82,188],"which":[83,107],"system":[85],"opts":[86],"trust":[88,149],"model.":[90],"Built":[91],"upon":[92],"conformal":[93],"inference":[94],"hypothesis":[96,142],"testing":[97,143],"ideas,":[98],"first":[100],"constructs":[101],"class":[103],"(generalized)":[105],"e-values,":[106],"are":[108],"non-negative":[109],"random":[110],"variables":[111],"whose":[112],"product":[113],"unknown":[116],"has":[118],"expectation":[119],"no":[120],"greater":[121],"than":[122],"one.":[123],"Such":[124],"property":[126],"ensured":[128],"by":[129],"data":[130],"exchangeability":[131],"without":[132],"requiring":[133],"modeling":[135],"assumptions.":[136],"Passing":[137],"these":[138],"e-values":[139],"procedures,":[144],"we":[145],"yield":[146],"binary":[148],"finite-sample":[152],"control.":[154],"avoids":[156],"need":[158],"uniform":[160],"concentration,":[161],"can":[163],"be":[164],"readily":[165],"extended":[166],"settings":[168],"distribution":[170],"shifts.":[171],"evaluate":[173],"proposed":[175],"methods":[176],"simulations":[178],"demonstrate":[180],"their":[181],"efficacy":[182],"through":[183],"applications":[184],"management":[187],"drug":[189],"discovery,":[190],"health":[191],"prediction,":[193],"large":[195],"language":[196],"models.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-28T00:00:00"}