{"id":"https://openalex.org/W7163044837","doi":"https://doi.org/10.48550/arxiv.2605.30447","title":"Calibrated Preference Learning: The Case of Label Ranking","display_name":"Calibrated Preference Learning: The Case of Label Ranking","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7163044837","doi":"https://doi.org/10.48550/arxiv.2605.30447"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30447","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30447","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.2605.30447","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137517439","display_name":"Santo M. A. R. Thies","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thies, Santo M. A. R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005338669","display_name":"Viktor Bengs","orcid":"https://orcid.org/0000-0001-6988-6186"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bengs, Viktor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014400024","display_name":"Timo Kaufmann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaufmann, Timo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082997556","display_name":"Sebasti\u00e1n Vollmer","orcid":"https://orcid.org/0000-0002-7863-0462"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vollmer, Sebastian J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137597798","display_name":"Eyke H\u00fcllermeier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H\u00fcllermeier, Eyke","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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.22300000488758087,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.22300000488758087,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.1851000040769577,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.09740000218153,"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/calibration","display_name":"Calibration","score":0.7505000233650208},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.7455999851226807},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6880999803543091},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6151999831199646},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6022999882698059},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.5705000162124634},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.47760000824928284},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.435699999332428}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.7505000233650208},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7455999851226807},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6880999803543091},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6151999831199646},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6022999882698059},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.5705000162124634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5544000267982483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5249000191688538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4984000027179718},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39660000801086426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36649999022483826},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33059999346733093},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.2851000130176544},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2678000032901764},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.26489999890327454},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.26179999113082886}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30447","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30447","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.2605.30447","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30447","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":0.6977238059043884}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Calibration,":[0],"the":[1,33,91,158],"alignment":[2],"of":[3,42,76,161],"predicted":[4],"probabilities":[5],"with":[6,114,138],"true":[7],"outcome":[8],"frequencies,":[9],"is":[10,35],"essential":[11],"for":[12,18,28,69],"reliable":[13],"decision-making.":[14],"While":[15],"extensively":[16],"studied":[17],"classification":[19],"and":[20,54,63,72,82,96,98,119,163],"regression,":[21],"calibration":[22,68,89,100,132],"has":[23],"not":[24,94,136],"been":[25],"formally":[26],"addressed":[27],"probabilistic":[29],"label":[30,44,70,107],"ranking,":[31],"where":[32],"goal":[34],"to":[36,56,125,166],"predict":[37],"a":[38,43,74,144],"distribution":[39],"over":[40],"orderings":[41],"set.":[45],"Naively":[46],"treating":[47],"rankings":[48],"as":[49,61],"classes":[50],"ignores":[51],"their":[52],"structure":[53],"fails":[55],"capture":[57],"important":[58],"modalities":[59],"such":[60],"pairwise":[62],"top-k":[64,83,99,120],"predictions.":[65],"We":[66,85],"formalize":[67],"ranking":[71,108],"develop":[73],"hierarchy":[75],"notions":[77],"covering":[78],"full":[79],"rankings,":[80],"sub-rankings,":[81],"rankings.":[84],"prove":[86],"that":[87,131],"full-rank":[88],"implies":[90],"others":[92],"but":[93,135],"conversely,":[95],"sub-ranking":[97,118],"are":[101,110],"incomparable.":[102],"Empirically,":[103],"we":[104,129],"find":[105,130],"popular":[106],"models":[109],"often":[111],"poorly":[112],"calibrated,":[113],"substantial":[115],"differences":[116],"between":[117],"metrics.":[121],"Applying":[122],"our":[123],"framework":[124],"RLHF":[126],"reward":[127],"models,":[128],"correlates":[133],"strongly":[134],"perfectly":[137],"benchmark":[139],"accuracy,":[140],"suggesting":[141],"it":[142],"captures":[143],"meaningful":[145],"quality":[146],"dimension":[147],"beyond":[148],"top-1":[149],"accuracy.":[150],"These":[151],"findings":[152],"motivate":[153],"future":[154],"work":[155],"on":[156],"understanding":[157],"downstream":[159],"effects":[160],"miscalibration":[162],"developing":[164],"methods":[165],"correct":[167],"it.":[168]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-02T00:00:00"}