{"id":"https://openalex.org/W7138873171","doi":"https://doi.org/10.48550/arxiv.2603.17139","title":"Contextual Preference Distribution Learning","display_name":"Contextual Preference Distribution Learning","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7138873171","doi":"https://doi.org/10.48550/arxiv.2603.17139"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.17139","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17139","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.17139","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129890410","display_name":"Benjamin Hudson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hudson, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129824804","display_name":"Laurent Charlin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Charlin, Laurent","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5046133578","display_name":"Emma Frejinger","orcid":"https://orcid.org/0000-0003-1930-607X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frejinger, Emma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.2921999990940094,"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"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.2921999990940094,"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/T11413","display_name":"Risk and Portfolio Optimization","score":0.10599999874830246,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.06830000132322311,"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/leverage","display_name":"Leverage (statistics)","score":0.6049000024795532},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.45559999346733093},{"id":"https://openalex.org/keywords/surprise","display_name":"Surprise","score":0.4505000114440918},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4239000082015991},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.41620001196861267},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.3644999861717224},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.36000001430511475},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.35580000281333923}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6049000024795532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5835000276565552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5259000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.501800000667572},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C2780343955","wikidata":"https://www.wikidata.org/wiki/Q333173","display_name":"Surprise","level":2,"score":0.4505000114440918},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.36000001430511475},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3149000108242035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31049999594688416},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3089999854564667},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.17139","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17139","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.17139","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17139","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7468838691711426,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Decision-making":[0],"problems":[1],"often":[2],"feature":[3],"uncertainty":[4],"stemming":[5],"from":[6,62],"heterogeneous":[7],"and":[8,25,56,146],"context-dependent":[9],"human":[10,39],"preferences.":[11],"To":[12],"address":[13],"this,":[14],"we":[15,89],"propose":[16],"a":[17,83,91,98,107,124,140],"sequential":[18],"learning-and-optimization":[19],"pipeline":[20],"to":[21,28,72,97,136,139,148,151],"learn":[22],"preference":[23],"distributions":[24],"leverage":[26],"them":[27,77],"solve":[29],"downstream":[30],"problems,":[31],"for":[32,79,115],"example":[33],"risk-averse":[34,80,153],"formulations.":[35],"We":[36],"focus":[37],"on":[38],"choice":[40,57],"settings":[41],"that":[42],"can":[43],"be":[44],"formulated":[45],"as":[46],"(integer)":[47],"linear":[48],"programs.":[49],"In":[50,123],"such":[51],"settings,":[52],"existing":[53],"inverse":[54],"optimization":[55,121],"modelling":[58],"methods":[59],"infer":[60],"preferences":[61],"observed":[63],"choices":[64],"but":[65],"typically":[66],"produce":[67],"point":[68],"estimates":[69],"or":[70],"fail":[71],"capture":[73],"contextual":[74,95],"shifts,":[75],"making":[76],"unsuitable":[78],"decision-making.":[81],"Using":[82],"bounded-variance":[84],"score":[85],"function":[86],"gradient":[87],"estimator,":[88],"train":[90],"predictive":[92],"model":[93,112],"mapping":[94],"features":[96],"rich":[99],"class":[100],"of":[101],"parameterizable":[102],"distributions.":[103],"This":[104],"approach":[105,129,142],"yields":[106],"maximum":[108],"likelihood":[109],"estimate.":[110],"The":[111],"generates":[113],"scenarios":[114],"unseen":[116],"contexts":[117],"in":[118],"the":[119],"subsequent":[120],"phase.":[122],"synthetic":[125],"ridesharing":[126],"environment,":[127],"our":[128],"reduces":[130],"average":[131],"post-decision":[132],"surprise":[133],"by":[134],"up":[135,147],"114$\\times$":[137],"compared":[138,150],"risk-neutral":[141],"with":[143],"perfect":[144],"predictions":[145],"25$\\times$":[149],"leading":[152],"baselines.":[154]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-20T00:00:00"}
