{"id":"https://openalex.org/W2918485313","doi":"https://doi.org/10.1109/acssc.2018.8645081","title":"Interactive Linear Regression with Pairwise Comparisons","display_name":"Interactive Linear Regression with Pairwise Comparisons","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2918485313","doi":"https://doi.org/10.1109/acssc.2018.8645081","mag":"2918485313"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2018.8645081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645081","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5006654661","display_name":"Yichong Xu","orcid":"https://orcid.org/0000-0002-1302-738X"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yichong Xu","raw_affiliation_strings":["Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079499739","display_name":"Sivaraman Balakrishnan","orcid":"https://orcid.org/0000-0002-4538-8665"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sivaraman Balakrishnan","raw_affiliation_strings":["Department of Statistics, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758181","display_name":"Aarti Singh","orcid":"https://orcid.org/0000-0002-8278-3672"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aarti Singh","raw_affiliation_strings":["Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037154494","display_name":"Artur Dubrawski","orcid":"https://orcid.org/0000-0002-2372-0831"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Artur Dubrawski","raw_affiliation_strings":["Autonlab, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Autonlab, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0781,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48590022,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"636","last_page":"640"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9997000098228455,"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":0.9997000098228455,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9987999796867371,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.996399998664856,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7676129341125488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7561522126197815},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.6697174310684204},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5856356024742126},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.511298656463623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4859405755996704},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4731995761394501},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4662420153617859},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35734060406684875},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2905627489089966},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17479372024536133},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1586878001689911}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7676129341125488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7561522126197815},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.6697174310684204},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5856356024742126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.511298656463623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4859405755996704},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4731995761394501},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4662420153617859},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35734060406684875},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2905627489089966},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17479372024536133},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1586878001689911}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2018.8645081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645081","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W146740654","https://openalex.org/W637450849","https://openalex.org/W1484867920","https://openalex.org/W1531719573","https://openalex.org/W1544444892","https://openalex.org/W1584796555","https://openalex.org/W1962925619","https://openalex.org/W2033442452","https://openalex.org/W2056707879","https://openalex.org/W2072782484","https://openalex.org/W2096733369","https://openalex.org/W2107808918","https://openalex.org/W2135046866","https://openalex.org/W2135327149","https://openalex.org/W2163371195","https://openalex.org/W2164492469","https://openalex.org/W2475016247","https://openalex.org/W2531197876","https://openalex.org/W2620484538","https://openalex.org/W2624688904","https://openalex.org/W2725329413","https://openalex.org/W2731232934","https://openalex.org/W2740717009","https://openalex.org/W2763110165","https://openalex.org/W2951836260","https://openalex.org/W2963050819","https://openalex.org/W2964287878","https://openalex.org/W2964321744","https://openalex.org/W3099206234","https://openalex.org/W4252213128","https://openalex.org/W6605920081","https://openalex.org/W6620664439","https://openalex.org/W6632665862","https://openalex.org/W6634869654","https://openalex.org/W6641110779","https://openalex.org/W6681840828","https://openalex.org/W6721000645","https://openalex.org/W6738869693","https://openalex.org/W6739174935","https://openalex.org/W6741264185","https://openalex.org/W6742024500","https://openalex.org/W6903518220"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2942366970","https://openalex.org/W2793211469","https://openalex.org/W2069885834","https://openalex.org/W2991340708","https://openalex.org/W31220157","https://openalex.org/W2288557197","https://openalex.org/W4233024177","https://openalex.org/W2101914902","https://openalex.org/W3174613421"],"abstract_inverted_index":{"A":[0],"general":[1],"goal":[2],"of":[3,11,19,30,39,94],"interactive":[4,71],"learning":[5,20],"is":[6,45,114],"to":[7,35,79,97,117],"investigate":[8],"broad":[9],"ways":[10],"leveraging":[12],"human":[13],"feedback,":[14],"and":[15,77,84,133],"understand":[16],"the":[17,108],"benefits":[18],"from":[21,42],"potentially":[22],"complex":[23],"feedback.":[24],"We":[25,68,101],"study":[26],"a":[27,81,90],"special":[28],"case":[29],"linear":[31,82],"regression":[32],"with":[33],"access":[34],"comparisons":[36,53,63,78],"between":[37],"pairs":[38],"samples.":[40],"Learning":[41],"such":[43],"queries":[44],"motivated":[46],"by":[47],"several":[48],"important":[49],"applications,":[50],"where":[51],"obtaining":[52],"can":[54,64,135],"be":[55,65,136],"much":[56],"easier":[57],"than":[58],"direct":[59,95],"labels,":[60],"and/or":[61],"when":[62,129],"more":[66],"reliable.":[67],"develop":[69],"an":[70],"algorithm":[72,113,125],"that":[73,86,111,123],"utilizes":[74],"both":[75],"labels":[76,96,130],"obtain":[80],"estimator,":[83],"show":[85,122],"it":[87,134],"only":[88],"requires":[89],"very":[91],"small":[92],"amount":[93],"achieve":[98],"low":[99],"error.":[100],"also":[102],"give":[103],"minimax":[104],"lower":[105],"bounds":[106],"for":[107,138],"problem,":[109],"showing":[110],"our":[112,124],"optimal":[115],"up":[116],"log":[118],"factors.":[119],"Finally,":[120],"experiments":[121],"outperforms":[126],"label-only":[127],"algorithms":[128],"are":[131],"scarce,":[132],"practical":[137],"real-world":[139],"applications.":[140]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}