{"id":"https://openalex.org/W2999232157","doi":"https://doi.org/10.48550/arxiv.2001.05497","title":"Noise-tolerant, Reliable Active Classification with Comparison Queries","display_name":"Noise-tolerant, Reliable Active Classification with Comparison Queries","publication_year":2020,"publication_date":"2020-01-15","ids":{"openalex":"https://openalex.org/W2999232157","doi":"https://doi.org/10.48550/arxiv.2001.05497","mag":"2999232157"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2001.05497","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.05497","pdf_url":"https://arxiv.org/pdf/2001.05497","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/2001.05497","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058877254","display_name":"Max Hopkins","orcid":"https://orcid.org/0000-0002-7695-6063","observed_orcids":["https://orcid.org/0000-0002-7695-6063"]},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hopkins, Max","raw_affiliation_strings":["Department of Computer Science and Engineering , UCSD , California , CA 92092","University of California\u2014San Diego"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering , UCSD , California , CA 92092","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"University of California\u2014San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035962853","display_name":"Daniel M. Kane","orcid":"https://orcid.org/0009-0007-9647-2609","observed_orcids":["https://orcid.org/0009-0007-9647-2609","https://orcid.org/0000-0002-0220-9962","https://orcid.org/0000-0002-5884-3487"]},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kane, Daniel","raw_affiliation_strings":["Department of Computer Science and Engineering /","University of California\u2014San Diego"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering /","institution_ids":[]},{"raw_affiliation_string":"University of California\u2014San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044999305","display_name":"Shachar Lovett","orcid":"https://orcid.org/0000-0003-4552-1443","observed_orcids":["https://orcid.org/0000-0003-4552-1443"]},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lovett, Shachar","raw_affiliation_strings":["Department of Computer Science and Engineering , UCSD , California , CA 92092","University of California\u2014San Diego"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering , UCSD , California , CA 92092","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"University of California\u2014San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031473871","display_name":"Gaurav Mahajan","orcid":"https://orcid.org/0000-0002-5646-0634","observed_orcids":["https://orcid.org/0000-0002-5646-0634"]},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mahajan, Gaurav","raw_affiliation_strings":["Department of Computer Science and Engineering , UCSD , California , CA 92092","Department of Mathematics , UCSD , California , CA 92092","University of California\u2014San Diego"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering , UCSD , California , CA 92092","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"Department of Mathematics , UCSD , California , CA 92092","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"University of California\u2014San Diego","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1957","last_page":"2006"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9984999895095825,"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":0.9984999895095825,"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":0.0003000000142492354,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/active-learning-machine-learning","display_name":"active learning (machine learning)","score":0.9739999771118164},{"id":"https://openalex.org/keywords/noisy-classification","display_name":"noisy classification","score":0.4970000088214874},{"id":"https://openalex.org/keywords/query-efficiency","display_name":"query efficiency","score":0.5979999899864197},{"id":"https://openalex.org/keywords/labeling-efficiency","display_name":"label efficiency","score":0.7200000286102295},{"id":"https://openalex.org/keywords/unlabeled-data","display_name":"unlabeled data","score":0.5049999952316284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7804398536682129},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6747153997421265},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.614939272403717},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.5708229541778564},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5454694032669067},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.5339813232421875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4840252101421356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4732701778411865},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4647983908653259},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.41266632080078125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34873396158218384},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34556281566619873},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1513039767742157},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:arXiv.org:2001.05497","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.05497","pdf_url":"https://arxiv.org/pdf/2001.05497","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":"pmh:oai:escholarship.org:ark:/13030/qt1jj8q55b","doi":null,"is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/1jj8q55b","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"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":"article"},{"id":"doi:10.48550/arxiv.2001.05497","doi":"https://doi.org/10.48550/arxiv.2001.05497","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2001.05497","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"},{"id":"mag:3046323564","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v125/hopkins20a/hopkins20a.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306418075","display_name":"Conference on Learning Theory","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Conference on Learning Theory","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2001.05497","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.05497","pdf_url":"https://arxiv.org/pdf/2001.05497","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":[{"id":"https://openalex.org/G1347492840","display_name":"Graduate Research Fellowship Program (GRFP)","funder_award_id":"1650112","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7988950302","display_name":"CAREER:  Structure and Analysis of Low Degree Polynomials","funder_award_id":"1553288","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G894745576","display_name":"AF: SMALL: Finding Models of Data and Mathematical Objects","funder_award_id":"1909634","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2999232157.pdf","grobid_xml":"https://content.openalex.org/works/W2999232157.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W116104437","https://openalex.org/W1584796555","https://openalex.org/W1597871016","https://openalex.org/W1983320747","https://openalex.org/W2019363670","https://openalex.org/W2049393399","https://openalex.org/W2062896907","https://openalex.org/W2076541756","https://openalex.org/W2107189314","https://openalex.org/W2108428725","https://openalex.org/W2118867735","https://openalex.org/W2128518360","https://openalex.org/W2151596305","https://openalex.org/W2475016247","https://openalex.org/W2488247662","https://openalex.org/W2611121736","https://openalex.org/W2752219186","https://openalex.org/W2770737599","https://openalex.org/W2780179343","https://openalex.org/W2791077206","https://openalex.org/W2959954876","https://openalex.org/W2962816257","https://openalex.org/W2963402790","https://openalex.org/W2964057462","https://openalex.org/W2964242659","https://openalex.org/W2977256954"],"related_works":["https://openalex.org/W2959954876","https://openalex.org/W2118867735","https://openalex.org/W3126350303","https://openalex.org/W7095827832","https://openalex.org/W2101164044","https://openalex.org/W1573735289","https://openalex.org/W1588027929","https://openalex.org/W2620857485","https://openalex.org/W7099776750","https://openalex.org/W7097634971"],"abstract_inverted_index":{"With":[0],"the":[1,10,33,56,75,100],"explosion":[2],"of":[3,35,46,58,68,99],"massive,":[4],"widely":[5],"available":[6],"unlabeled":[7],"data":[8,47],"in":[9,26,29,38,55],"past":[11],"years,":[12],"finding":[13],"label":[14,54],"and":[15,28,78,106],"time":[16,77],"efficient,":[17],"robust":[18,87],"learning":[19,83],"algorithms":[20,40,81,95],"has":[21],"become":[22],"ever":[23],"more":[24],"important":[25],"theory":[27],"practice.":[30],"We":[31,92],"study":[32],"paradigm":[34],"active":[36],"learning,":[37],"which":[39],"with":[41,121,132],"access":[42],"to":[43,53,88],"large":[44],"pools":[45],"may":[48],"adaptively":[49],"choose":[50],"what":[51],"samples":[52],"hope":[57],"exponentially":[59],"increasing":[60],"efficiency.":[61],"By":[62],"introducing":[63],"comparisons,":[64],"an":[65],"additional":[66],"type":[67],"query":[69,79],"comparing":[70],"two":[71],"points,":[72],"we":[73],"provide":[74,94,110],"first":[76],"efficient":[80],"for":[82,96],"non-homogeneous":[84],"linear":[85],"separators":[86],"bounded":[89],"(Massart)":[90],"noise.":[91],"further":[93],"a":[97,111,126],"generalization":[98],"popular":[101],"Tsybakov":[102],"low":[103],"noise":[104],"condition,":[105],"show":[107],"how":[108],"comparisons":[109],"strong":[112],"reliability":[113],"guarantee":[114],"that":[115,128],"is":[116],"often":[117],"impractical":[118],"or":[119],"impossible":[120],"only":[122],"labels":[123],"-":[124],"returning":[125],"classifier":[127],"makes":[129],"no":[130],"errors":[131],"high":[133],"probability.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2020-01-23T00:00:00"}