{"id":"https://openalex.org/W2156656573","doi":null,"title":"Self-calibrating Probability Forecasting","display_name":"Self-calibrating Probability Forecasting","publication_year":2003,"publication_date":"2003-12-09","ids":{"openalex":"https://openalex.org/W2156656573","mag":"2156656573"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.104.8322","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.8322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.vovk.net/cp/09.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063978613","display_name":"Vladimir Vovk","orcid":"https://orcid.org/0000-0003-2602-6877","observed_orcids":["https://orcid.org/0000-0003-2602-6877"]},"institutions":[{"id":"https://openalex.org/I184558857","display_name":"Royal Holloway University of London","ror":"https://ror.org/04g2vpn86","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I184558857"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Vladimir Vovk","raw_affiliation_strings":["Computer Learning Research Centre, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK"],"raw_orcid":"https://orcid.org/0000-0003-2602-6877","affiliations":[{"raw_affiliation_string":"Computer Learning Research Centre, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK","institution_ids":["https://openalex.org/I184558857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105842094","display_name":"Glenn R. Shafer","orcid":"https://orcid.org/0000-0003-2148-2451","observed_orcids":["https://orcid.org/0000-0003-2148-2451"]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Glenn Shafer","raw_affiliation_strings":["Rutgers School of Business, Newark and New Brunswick, 180 University Avenue, Newark, NJ#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers School of Business, Newark and New Brunswick, 180 University Avenue, Newark, NJ#TAB#","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086768742","display_name":"Ilia Nouretdinov","orcid":"https://orcid.org/0000-0002-5968-7228","observed_orcids":["https://orcid.org/0000-0002-5968-7228"]},"institutions":[{"id":"https://openalex.org/I184558857","display_name":"Royal Holloway University of London","ror":"https://ror.org/04g2vpn86","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I184558857"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ilia Nouretdinov","raw_affiliation_strings":["Computer Learning Research Centre, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK"],"raw_orcid":"https://orcid.org/0000-0002-5968-7228","affiliations":[{"raw_affiliation_string":"Computer Learning Research Centre, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK","institution_ids":["https://openalex.org/I184558857"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6927,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.73653949,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":"16","issue":null,"first_page":"1133","last_page":"1140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.7925000190734863,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.7925000190734863,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.031700000166893005,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.029100000858306885,"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/probabilistic-forecasting","display_name":"probabilistic forecasting","score":0.9819999933242798},{"id":"https://openalex.org/keywords/e-learning","display_name":"online learning","score":0.7099999785423279},{"id":"https://openalex.org/keywords/1-nearest-neighbor","display_name":"1-nearest neighbor","score":0.8059999942779541},{"id":"https://openalex.org/keywords/conditional-probability-estimation","display_name":"conditional probability estimation","score":0.8999999761581421},{"id":"https://openalex.org/keywords/i-i-d-data","display_name":"i.i.d. data","score":0.6389999985694885}],"concepts":[{"id":"https://openalex.org/C21031990","wikidata":"https://www.wikidata.org/wiki/Q355020","display_name":"Probability measure","level":2,"score":0.697462797164917},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.6800304651260376},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.6336595416069031},{"id":"https://openalex.org/C49698424","wikidata":"https://www.wikidata.org/wiki/Q1989192","display_name":"Law of total probability","level":4,"score":0.5558772087097168},{"id":"https://openalex.org/C97933134","wikidata":"https://www.wikidata.org/wiki/Q5374249","display_name":"Empirical probability","level":4,"score":0.5531637668609619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5469000339508057},{"id":"https://openalex.org/C55508974","wikidata":"https://www.wikidata.org/wiki/Q190763","display_name":"Venn diagram","level":2,"score":0.5412411689758301},{"id":"https://openalex.org/C103982235","wikidata":"https://www.wikidata.org/wiki/Q7309594","display_name":"Regular conditional probability","level":4,"score":0.5386348366737366},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5319766402244568},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5214219093322754},{"id":"https://openalex.org/C72396439","wikidata":"https://www.wikidata.org/wiki/Q16000505","display_name":"Symmetric probability distribution","level":3,"score":0.5172515511512756},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5144493579864502},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4887580871582031},{"id":"https://openalex.org/C197096303","wikidata":"https://www.wikidata.org/wiki/Q869887","display_name":"Probability mass function","level":3,"score":0.4866977035999298},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.4704899787902832},{"id":"https://openalex.org/C130648207","wikidata":"https://www.wikidata.org/wiki/Q6007562","display_name":"Imprecise probability","level":3,"score":0.43721258640289307},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4213337302207947},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4133395552635193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.334129273891449},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2973659336566925},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.2325117588043213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2202456295490265},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17578652501106262},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.15253391861915588},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.104.8322","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.8322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.vovk.net/cp/09.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.68.6829","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.6829","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://books.nips.cc/papers/files/nips16/NIPS2003_LT09.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.83.5162","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.5162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://books.nips.cc/papers/files/nips16/preproc/NIPS2003_LT09.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.858.4860","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.858.4860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://papers.nips.cc/paper/2462-self-calibrating-probability-forecasting.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.9.3530","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.3530","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://books.nips.cc/papers/files/nips16/NIPS2003_LT09.ps.gz","raw_type":"text"},{"id":"mag:2156656573","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/2462-self-calibrating-probability-forecasting.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W65382156","https://openalex.org/W135042828","https://openalex.org/W608863562","https://openalex.org/W1488642921","https://openalex.org/W1559516636","https://openalex.org/W1564947197","https://openalex.org/W1661212201","https://openalex.org/W2014384147","https://openalex.org/W2106491486","https://openalex.org/W2128585330","https://openalex.org/W2130104753","https://openalex.org/W2492953829"],"related_works":["https://openalex.org/W2886533286","https://openalex.org/W2583346933","https://openalex.org/W6986517343","https://openalex.org/W1973647602","https://openalex.org/W2098824882","https://openalex.org/W2040801326","https://openalex.org/W1950435585","https://openalex.org/W1552184565","https://openalex.org/W3181339911","https://openalex.org/W4255572858"],"abstract_inverted_index":{"In":[0],"the":[1,6,25,29,45,51,61,70,127,170,179,184],"problem":[2],"of":[3,28,60,63,72,102,118,157,178],"probability":[4,22,37,88,119,133,144,166,181],"forecasting":[5,38,89],"learner's":[7],"goal":[8],"is":[9,39,91],"to":[10,41,111],"output,":[11],"given":[12],"a":[13,17,20,56,99,113,140,150,163],"training":[14],"set":[15],"and":[16,79,155,175],"new":[18,30],"object,":[19],"suitable":[21],"measure":[23,182],"on":[24,149],"possible":[26],"values":[27],"object's":[31],"label.":[32],"An":[33],"on-line":[34],"algorithm":[35,90],"for":[36,104],"said":[40],"be":[42],"well-calibrated":[43,93],"if":[44],"probabilities":[46,173],"it":[47],"outputs":[48],"agree":[49],"with":[50],"observed":[52],"frequencies.":[53],"We":[54],"give":[55],"natural":[57],"nonasymptotic":[58],"formalization":[59],"notion":[62],"well-calibratedness,":[64],"which":[65,121],"we":[66,125],"then":[67],"study":[68],"under":[69],"assumption":[71],"randomness":[73],"(the":[74],"object/label":[75],"pairs":[76],"are":[77,109],"independent":[78],"identically":[80],"distributed).":[81],"It":[82],"turns":[83],"out":[84],"that,":[85],"although":[86],"no":[87],"automatically":[92],"in":[94,129],"our":[95,158],"sense,":[96],"there":[97],"exists":[98],"wide":[100],"class":[101,131],"algorithms":[103,108,128],"\"multiprobability":[105],"forecasting\"":[106],"(such":[107],"allowed":[110],"output":[112],"set,":[114,154],"ideally":[115],"very":[116],"narrow,":[117],"measures)":[120],"satisfy":[122],"this":[123,130],"property;":[124],"call":[126],"\"Venn":[132],"machines\".":[134],"Our":[135],"experimental":[136],"results":[137,160],"demonstrate":[138],"that":[139,162],"1-Nearest":[141],"Neighbor":[142],"Venn":[143,165],"machine":[145,167],"performs":[146],"reasonably":[147],"well":[148],"standard":[151],"benchmark":[152],"data":[153],"one":[156],"theoretical":[159],"asserts":[161],"simple":[164],"asymptotically":[168],"approaches":[169],"true":[171,180],"conditional":[172],"regardless,":[174],"without":[176],"knowledge,":[177],"generating":[183],"examples.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":6},{"year":2012,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}
