{"id":"https://openalex.org/W7147676018","doi":"https://doi.org/10.48550/arxiv.2603.27414","title":"Multiple-Prediction-Powered Inference","display_name":"Multiple-Prediction-Powered Inference","publication_year":2026,"publication_date":"2026-03-28","ids":{"openalex":"https://openalex.org/W7147676018","doi":"https://doi.org/10.48550/arxiv.2603.27414"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27414","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27414","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.2603.27414","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132594326","display_name":"Charlie Cowen-Breen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cowen-Breen, Charlie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036435487","display_name":"Alekh Agarwal","orcid":"https://orcid.org/0000-0001-7032-7162"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agarwal, Alekh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132654605","display_name":"Stephen Bates","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bates, Stephen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051617344","display_name":"William W. Cohen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cohen, William W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047699861","display_name":"Jacob Eisenstein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eisenstein, Jacob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047817959","display_name":"Amir Globerson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Globerson, Amir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5079422282","display_name":"Adam Fisch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fisch, Adam","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.18979999423027039,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.18979999423027039,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10028","display_name":"Topic Modeling","score":0.1386999934911728,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.09030000120401382,"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/inference","display_name":"Inference","score":0.6521000266075134},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.6265000104904175},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.5223000049591064},{"id":"https://openalex.org/keywords/fiducial-inference","display_name":"Fiducial inference","score":0.4779999852180481},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.4235999882221222},{"id":"https://openalex.org/keywords/normality","display_name":"Normality","score":0.38350000977516174}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6521000266075134},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6265000104904175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5976999998092651},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.5223000049591064},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.4779999852180481},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.460999995470047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4271000027656555},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.3797000050544739},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30630001425743103},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26809999346733093},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27414","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27414","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.2603.27414","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27414","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Statistical":[0],"estimation":[1,73],"often":[2],"involves":[3],"tradeoffs":[4],"between":[5],"expensive,":[6],"high-quality":[7],"measurements":[8],"and":[9,48,97],"a":[10,20],"variety":[11],"of":[12,51,90],"lower-quality":[13],"proxies.":[14],"We":[15],"introduce":[16],"Multiple-Prediction-Powered":[17],"Inference":[18],"(MultiPPI):":[19],"general":[21],"framework":[22],"for":[23],"constructing":[24],"statistically":[25],"efficient":[26],"estimates":[27],"by":[28,92],"optimally":[29],"allocating":[30],"resources":[31],"across":[32,57],"these":[33],"diverse":[34,59],"data":[35],"sources.":[36],"This":[37,78],"work":[38],"provides":[39],"theoretical":[40],"guarantees":[41],"about":[42],"the":[43,52],"minimax":[44],"optimality,":[45],"finite-sample":[46],"performance,":[47],"asymptotic":[49],"normality":[50],"MultiPPI":[53,69],"estimator.":[54],"Through":[55],"experiments":[56],"three":[58],"large":[60],"language":[61],"model":[62],"(LLM)":[63],"evaluation":[64],"scenarios,":[65],"we":[66],"show":[67],"that":[68],"consistently":[70],"achieves":[71],"lower":[72],"error":[74],"than":[75],"existing":[76],"baselines.":[77],"advantage":[79],"stems":[80],"from":[81],"its":[82],"budget-adaptive":[83],"allocation":[84],"strategy,":[85],"which":[86],"strategically":[87],"combines":[88],"subsets":[89],"models":[91],"learning":[93],"their":[94],"complex":[95],"cost":[96],"correlation":[98],"structures.":[99]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
