{"id":"https://openalex.org/W3203129694","doi":"https://doi.org/10.48550/arxiv.2110.00816","title":"Calibrated Multiple-Output Quantile Regression with Representation Learning","display_name":"Calibrated Multiple-Output Quantile Regression with Representation Learning","publication_year":2021,"publication_date":"2021-10-02","ids":{"openalex":"https://openalex.org/W3203129694","doi":"https://doi.org/10.48550/arxiv.2110.00816","mag":"3203129694"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2110.00816","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.00816","pdf_url":"https://arxiv.org/pdf/2110.00816","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/2110.00816","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000640911","display_name":"Shai Feldman","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Feldman, Shai","raw_affiliation_strings":["Department of Computer Science Technion-Israel Institute of Technology Technion City , Haifa 32000 , Israel","Technion-Israel Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Technion-Israel Institute of Technology Technion City , Haifa 32000 , Israel","institution_ids":["https://openalex.org/I174306211"]},{"raw_affiliation_string":"Technion-Israel Institute of Technology","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045046961","display_name":"Stephen Bates","orcid":"https://orcid.org/0000-0002-3273-8179","observed_orcids":["https://orcid.org/0000-0002-3273-8179"]},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["IL","US"],"is_corresponding":false,"raw_author_name":"Bates, Stephen","raw_affiliation_strings":["Department of Computer Science Technion-Israel Institute of Technology Technion City , Haifa 32000 , Israel","Departments of Electrical Engineering and Computer Science and","University of California\u2013Berkeley"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Technion-Israel Institute of Technology Technion City , Haifa 32000 , Israel","institution_ids":["https://openalex.org/I174306211"]},{"raw_affiliation_string":"Departments of Electrical Engineering and Computer Science and","institution_ids":[]},{"raw_affiliation_string":"University of California\u2013Berkeley","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041723842","display_name":"Yaniv Romano","orcid":"https://orcid.org/0000-0001-8951-8583","observed_orcids":["https://orcid.org/0000-0001-8951-8583","https://orcid.org/0000-0001-9031-8829"]},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["IL","US"],"is_corresponding":false,"raw_author_name":"Romano, Yaniv","raw_affiliation_strings":["Departments of Electrical and Computer Engineering and of Computer Science Technion-Israel Institute of Technology Technion City , Haifa 32000 , Israel","of Statistics University of California , Berkeley Berkeley , CA 94720 , USA","Technion-Israel Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departments of Electrical and Computer Engineering and of Computer Science Technion-Israel Institute of Technology Technion City , Haifa 32000 , Israel","institution_ids":["https://openalex.org/I174306211"]},{"raw_affiliation_string":"of Statistics University of California , Berkeley Berkeley , CA 94720 , USA","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"Technion-Israel Institute of Technology","institution_ids":["https://openalex.org/I174306211"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":22,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.17919999361038208,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.17919999361038208,"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/T10136","display_name":"Statistical Methods and Inference","score":0.13819999992847443,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.066600002348423,"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/conformal-prediction","display_name":"conformal prediction","score":0.8550000190734863},{"id":"https://openalex.org/keywords/multivariate-response","display_name":"multivariate response","score":0.7839999794960022},{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.8859999775886536},{"id":"https://openalex.org/keywords/deep-generative-models","display_name":"deep generative models","score":0.9919999837875366}],"concepts":[{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.649453341960907},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.5983163118362427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.577925980091095},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.5496417880058289},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5292009115219116},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4695079028606415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4407515525817871},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37128955125808716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3301963210105896},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24190348386764526},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2110.00816","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.00816","pdf_url":"https://arxiv.org/pdf/2110.00816","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":"mag:3203129694","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.00816.pdf","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2110.00816","doi":"https://doi.org/10.48550/arxiv.2110.00816","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2110.00816","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2110.00816","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.00816","pdf_url":"https://arxiv.org/pdf/2110.00816","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/W3203129694.pdf","grobid_xml":"https://content.openalex.org/works/W3203129694.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1553101044","https://openalex.org/W1573647811","https://openalex.org/W1965555277","https://openalex.org/W1977556410","https://openalex.org/W1987133051","https://openalex.org/W1991452110","https://openalex.org/W2010910251","https://openalex.org/W2032938012","https://openalex.org/W2049915260","https://openalex.org/W2050076271","https://openalex.org/W2123838014","https://openalex.org/W2136658108","https://openalex.org/W2167250202","https://openalex.org/W2168187896","https://openalex.org/W2188365844","https://openalex.org/W2222525945","https://openalex.org/W2337982825","https://openalex.org/W2739751836","https://openalex.org/W2963090522","https://openalex.org/W2963799213","https://openalex.org/W2964121744","https://openalex.org/W2969326543","https://openalex.org/W2971169014","https://openalex.org/W2974095354","https://openalex.org/W2992005611","https://openalex.org/W3007132401","https://openalex.org/W3027782004","https://openalex.org/W3037226294","https://openalex.org/W3037622168","https://openalex.org/W3048764598","https://openalex.org/W3099796618","https://openalex.org/W3103465772","https://openalex.org/W3118640283","https://openalex.org/W3127574654","https://openalex.org/W3145149967","https://openalex.org/W3169596881","https://openalex.org/W3170774461","https://openalex.org/W4361012985","https://openalex.org/W4410803261"],"related_works":["https://openalex.org/W7172488701","https://openalex.org/W4406692357","https://openalex.org/W4414926993","https://openalex.org/W7106228543","https://openalex.org/W7172365527","https://openalex.org/W7213438111","https://openalex.org/W4415334585","https://openalex.org/W4407090046","https://openalex.org/W4404351658","https://openalex.org/W4402819876"],"abstract_inverted_index":{"We":[0],"develop":[1],"a":[2,10,15,28,34,41,78,89,116],"method":[3,111,144],"to":[4,32,67,103,112,152],"generate":[5],"predictive":[6],"regions":[7,146],"that":[8,39,83,91,108,142,147],"cover":[9],"multivariate":[11,105],"response":[12,38,106],"variable":[13],"with":[14,115],"user-specified":[16],"probability.":[17],"Our":[18],"work":[19],"is":[20,123],"composed":[21],"of":[22,36,71,100],"two":[23],"components.":[24],"First,":[25],"we":[26,55,96],"use":[27],"deep":[29],"generative":[30],"model":[31],"learn":[33],"representation":[35],"the":[37,59,65,68,72,104,127],"has":[40],"unimodal":[42],"distribution.":[43,132],"Existing":[44],"multiple-output":[45],"quantile":[46],"regression":[47],"approaches":[48],"are":[49,148],"effective":[50],"in":[51,77,126],"such":[52],"cases,":[53],"so":[54],"apply":[56],"them":[57],"on":[58,135],"learned":[60],"representation,":[61],"and":[62,80,138],"then":[63],"transform":[64],"solution":[66],"original":[69],"space":[70],"response.":[73],"This":[74],"process":[75],"results":[76],"flexible":[79],"informative":[81],"region":[82],"can":[84],"have":[85],"an":[86,98],"arbitrary":[87],"shape,":[88],"property":[90],"existing":[92,153],"methods":[93],"lack.":[94],"Second,":[95],"propose":[97],"extension":[99],"conformal":[101],"prediction":[102],"setting":[107],"modifies":[109],"any":[110,131],"return":[113],"sets":[114],"pre-specified":[117],"coverage":[118,122],"level.":[119],"The":[120],"desired":[121],"theoretically":[124],"guaranteed":[125],"finite-sample":[128],"case":[129],"for":[130],"Experiments":[133],"conducted":[134],"both":[136],"real":[137],"synthetic":[139],"data":[140],"show":[141],"our":[143],"constructs":[145],"significantly":[149],"smaller":[150],"compared":[151],"techniques.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-10-11T00:00:00"}