{"id":"https://openalex.org/W7223245381","doi":"https://doi.org/10.48550/arxiv.2610.29416","title":"Alternative Strategies for Active Learning with Bayesian Bradley Terry Model","display_name":"Alternative Strategies for Active Learning with Bayesian Bradley Terry Model","publication_year":2026,"publication_date":"2026-10-21","ids":{"openalex":"https://openalex.org/W7223245381","doi":"https://doi.org/10.48550/arxiv.2610.29416"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.29416","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.29416","pdf_url":"https://arxiv.org/pdf/2610.29416","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.29416","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134064309","display_name":"Saman Jayasinghe","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I39586589","display_name":"Swansea University","ror":"https://ror.org/053fq8t95","country_code":"GB","type":"education","lineage":["https://openalex.org/I39586589"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jayasinghe, Saman","raw_affiliation_strings":["Swansea University , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swansea University , United Kingdom","institution_ids":["https://openalex.org/I39586589"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022708495","display_name":"Sean Peter Walton","orcid":"https://orcid.org/0000-0002-6451-265X","observed_orcids":["https://orcid.org/0000-0002-6451-265X"]},"institutions":[{"id":"https://openalex.org/I39586589","display_name":"Swansea University","ror":"https://ror.org/053fq8t95","country_code":"GB","type":"education","lineage":["https://openalex.org/I39586589"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Walton, Sean","raw_affiliation_strings":["Swansea University , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swansea University , United Kingdom","institution_ids":["https://openalex.org/I39586589"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159271509","display_name":"Alma Rahat","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I143804889","display_name":"Loughborough University","ror":"https://ror.org/04vg4w365","country_code":"GB","type":"education","lineage":["https://openalex.org/I143804889"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Rahat, Alma","raw_affiliation_strings":["Loughborough University , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Loughborough University , United Kingdom","institution_ids":["https://openalex.org/I143804889"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065882234","display_name":"Andy Gray","orcid":"https://orcid.org/0000-0002-1150-2052","observed_orcids":["https://orcid.org/0000-0002-1150-2052"]},"institutions":[{"id":"https://openalex.org/I91047523","display_name":"Bath Spa University","ror":"https://ror.org/0038jbq24","country_code":"GB","type":"education","lineage":["https://openalex.org/I91047523"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gray, Andy","raw_affiliation_strings":["Bath Spa University , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bath Spa University , United Kingdom","institution_ids":["https://openalex.org/I91047523"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068814790","display_name":"Chris Wheadon","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wheadon, Chris","raw_affiliation_strings":["No More Marking Ltd , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"No More Marking Ltd , United Kingdom","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.15520000457763672,"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":0.15520000457763672,"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/T10467","display_name":"Psychometric Methodologies and Testing","score":0.1096000000834465,"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"}},{"id":"https://openalex.org/T10991","display_name":"Game Theory and Voting Systems","score":0.10719999670982361,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/active-learning-machine-learning","display_name":"active learning (machine learning)","score":0.9139999747276306},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"pairwise comparison","score":0.6460000276565552},{"id":"https://openalex.org/keywords/knowledge-gradient","display_name":"knowledge gradient","score":0.7979999780654907},{"id":"https://openalex.org/keywords/polya-gamma-data-augmentation","display_name":"P\u00f3lya-Gamma data augmentation","score":0.9399999976158142},{"id":"https://openalex.org/keywords/acquisition-functions","display_name":"acquisition functions","score":0.625}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6428999900817871},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6195999979972839},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.60589998960495},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5820000171661377},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5756000280380249},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5691999793052673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4767000079154968},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.436599999666214},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4341999888420105},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42969998717308044},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37700000405311584},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.34850001335144043},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.334199994802475},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.334199994802475},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3158999979496002},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.27810001373291016},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.26820001006126404},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2621999979019165},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.257999986410141},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.29416","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.29416","pdf_url":"https://arxiv.org/pdf/2610.29416","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.29416","doi":"https://doi.org/10.48550/arxiv.2610.29416","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.29416","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.29416","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.29416","pdf_url":"https://arxiv.org/pdf/2610.29416","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223245381.pdf","grobid_xml":"https://content.openalex.org/works/W7223245381.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W2774697710","https://openalex.org/W6910689181","https://openalex.org/W7127137417"],"related_works":["https://openalex.org/W2152408869","https://openalex.org/W4386239631","https://openalex.org/W2964287878","https://openalex.org/W4399040387","https://openalex.org/W4417129249","https://openalex.org/W2547116930","https://openalex.org/W4417525782","https://openalex.org/W2982366565","https://openalex.org/W2164545125","https://openalex.org/W6960068619"],"abstract_inverted_index":{"Comparative":[0],"judgement":[1,148],"uses":[2,36],"pairwise":[3,157],"comparisons":[4,192],"to":[5,83,110,152,203],"rank":[6,75],"items,":[7],"but":[8],"recovering":[9,245],"accurate":[10],"rankings":[11],"from":[12,247],"a":[13,30,123,209,214],"limited":[14],"number":[15],"of":[16,90,105,118,199,220,236],"judgements":[17],"depends":[18],"on":[19,59,177,228],"both":[20],"the":[21,25,60,87,103,156,169,173,197,234],"inference":[22,238],"method":[23],"and":[24,44,50,77,135,239],"pairs":[26],"selected.":[27],"We":[28,52,65,94],"developed":[29],"Bayesian":[31,146,237],"Bradley-Terry":[32],"model":[33],"(BBTM)":[34],"that":[35],"Polya-Gamma":[37],"data":[38],"augmentation":[39],"for":[40,244],"efficient":[41],"Gibbs":[42],"sampling":[43],"quantifies":[45],"uncertainty":[46],"in":[47,164,188],"latent":[48],"abilities":[49,108],"ranks.":[51],"propose":[53],"three":[54],"active":[55,240],"learning":[56,241],"strategies":[57],"based":[58,176],"knowledge":[61],"gradient":[62],"(KG)":[63],"principle.":[64],"define":[66],"our":[67],"acquisition":[68],"functions":[69],"using":[70],"Kendall's":[71],"tau":[72],"distance,":[73],"posterior":[74,78,84,106,111,178],"entropy,":[76],"separation.":[79],"Approximate":[80],"one-step":[81],"updates":[82],"moments":[85],"reduce":[86],"computational":[88],"cost":[89],"evaluating":[91],"candidate":[92],"pairs.":[93],"also":[95],"introduce":[96],"Posterior":[97],"Separation":[98],"Reliability":[99],"(PSR),":[100],"which":[101],"measures":[102],"spread":[104],"mean":[107],"relative":[109],"uncertainty.":[112],"It":[113],"provides":[114],"an":[115],"internal":[116],"indicator":[117],"ranking":[119,141,165,189,229],"reliability":[120],"without":[121],"requiring":[122],"reference":[124],"ranking.":[125],"In":[126,213],"synthetic":[127],"experiments":[128,195],"covering":[129],"different":[130],"item":[131],"counts,":[132],"comparison":[133,211],"budgets,":[134],"noise":[136,154,171,216],"levels,":[137,155,172],"BBTM":[138],"generally":[139],"recovers":[140],"more":[142],"accurately":[143],"than":[144],"preference-based":[145],"comparative":[147],"(PBCJ).":[149],"At":[150],"low":[151],"moderate":[153,215],"entropy":[158],"pair":[159,242],"selection":[160,243],"strategy":[161,175],"performs":[162],"best":[163],"recovery.":[166],"However,":[167],"at":[168],"highest":[170],"KG":[174],"separation":[179],"produces":[180],"better":[181],"point":[182],"rankings.":[183],"PSR":[184],"increases":[185],"alongside":[186],"improvements":[187],"accuracy":[190,206],"as":[191],"accumulate.":[193],"Robustness":[194],"identify":[196],"proportion":[198],"informative":[200],"outcomes":[201,221],"needed":[202],"meet":[204],"specified":[205],"targets":[207],"within":[208],"fixed":[210],"budget.":[212],"setting,":[217],"replacing":[218],"30\\%":[219],"with":[222],"random":[223],"choices":[224],"has":[225],"little":[226],"effect":[227],"performance.":[230],"These":[231],"findings":[232],"demonstrate":[233],"value":[235],"ranks":[246],"limited,":[248],"noisy":[249],"paired":[250],"comparisons.":[251]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-23T00:00:00"}