{"id":"https://openalex.org/W2804368052","doi":"https://doi.org/10.48550/arxiv.1805.10407","title":"Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance","display_name":"Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance","publication_year":2018,"publication_date":"2018-05-26","ids":{"openalex":"https://openalex.org/W2804368052","doi":"https://doi.org/10.48550/arxiv.1805.10407","mag":"2804368052"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1805.10407","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1805.10407","pdf_url":"https://arxiv.org/pdf/1805.10407","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/1805.10407","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065068490","display_name":"Neal Jean","orcid":"https://orcid.org/0000-0002-2616-3348","observed_orcids":["https://orcid.org/0000-0002-2616-3348"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jean, Neal","raw_affiliation_strings":["Department of Computer Science Stanford University Stanford , CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Stanford University Stanford , CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069097726","display_name":"Sang Michael Xie","orcid":"https://orcid.org/0000-0002-0820-2753","observed_orcids":["https://orcid.org/0000-0002-0820-2753"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xie, Sang Michael","raw_affiliation_strings":["Department of Computer Science Stanford University Stanford , CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Stanford University Stanford , CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091179481","display_name":"Stefano Ermon","orcid":"https://orcid.org/0000-0003-0039-2887","observed_orcids":["https://orcid.org/0000-0003-0039-2887"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ermon, Stefano","raw_affiliation_strings":["Department of Computer Science Stanford University Stanford , CA 94305","Stanford University ()"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science Stanford University Stanford , CA 94305","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Stanford University ()","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":null,"first_page":"5322","last_page":"5333"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.7728999853134155,"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":0.7728999853134155,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.2110999971628189,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.006000000052154064,"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/semi-supervised-regression","display_name":"semi-supervised regression","score":0.8360000252723694},{"id":"https://openalex.org/keywords/posterior-regularization","display_name":"posterior regularization","score":0.9629999995231628},{"id":"https://openalex.org/keywords/gaussian-processes","display_name":"Gaussian processes","score":0.9039999842643738},{"id":"https://openalex.org/keywords/unlabeled-data","display_name":"unlabeled data","score":0.7929999828338623},{"id":"https://openalex.org/keywords/hierarchical-representation-learning","display_name":"hierarchical feature learning","score":0.9110000133514404},{"id":"https://openalex.org/keywords/variational-autoencoder","display_name":"variational autoencoder","score":0.550000011920929},{"id":"https://openalex.org/keywords/mean-teacher","display_name":"mean teacher","score":0.9729999899864197}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6227322816848755},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5953689813613892},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5770268440246582},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.560865581035614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5140455961227417},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4735495150089264},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4612504541873932},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.44453486800193787},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4415639042854309},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.4110134541988373},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3744050860404968},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3602811098098755},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.06016841530799866},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1805.10407","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1805.10407","pdf_url":"https://arxiv.org/pdf/1805.10407","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.1805.10407","doi":"https://doi.org/10.48550/arxiv.1805.10407","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1805.10407","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:2964273603","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1805.10407.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":null,"is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1805.10407","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1805.10407","pdf_url":"https://arxiv.org/pdf/1805.10407","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/G2781775652","display_name":"CAREER: Modeling and Inference for Large Scale Spatio-Temporal Data","funder_award_id":"1651565","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5785822102","display_name":"Collaborative Research: CompSustNet: Expanding the Horizons of Computational Sustainability","funder_award_id":"1522054","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7963269316","display_name":"AitF: Collaborative Research: Efficient High-Dimensional Integration using Error-Correcting Codes","funder_award_id":"1733686","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"},{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320333566","display_name":"National Defense Science and Engineering Graduate","ror":null},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2804368052.pdf","grobid_xml":"https://content.openalex.org/works/W2804368052.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W57680428","https://openalex.org/W92894758","https://openalex.org/W769612788","https://openalex.org/W830076066","https://openalex.org/W1595698486","https://openalex.org/W1630959083","https://openalex.org/W1702387805","https://openalex.org/W1746819321","https://openalex.org/W1945616565","https://openalex.org/W1959608418","https://openalex.org/W1990334093","https://openalex.org/W2048679005","https://openalex.org/W2099471712","https://openalex.org/W2102419107","https://openalex.org/W2114188922","https://openalex.org/W2114718442","https://openalex.org/W2145494108","https://openalex.org/W2154368244","https://openalex.org/W2164928682","https://openalex.org/W2271840356","https://openalex.org/W2280377497","https://openalex.org/W2513506629","https://openalex.org/W2530816535","https://openalex.org/W2542136887","https://openalex.org/W2549019841","https://openalex.org/W2592691248","https://openalex.org/W2604645045","https://openalex.org/W2786559811","https://openalex.org/W2807323495","https://openalex.org/W2809657535","https://openalex.org/W2951004968","https://openalex.org/W2962964508","https://openalex.org/W2963207607","https://openalex.org/W2963324816","https://openalex.org/W2963578416","https://openalex.org/W2963641944","https://openalex.org/W2963711523","https://openalex.org/W2964121744","https://openalex.org/W2964135722","https://openalex.org/W2964159205","https://openalex.org/W3120740533"],"related_works":["https://openalex.org/W2114027834","https://openalex.org/W4229004907","https://openalex.org/W2158168589","https://openalex.org/W4403586131","https://openalex.org/W2322825644","https://openalex.org/W4382239155","https://openalex.org/W1969146802","https://openalex.org/W3082449787","https://openalex.org/W4308235628","https://openalex.org/W4321472433"],"abstract_inverted_index":{"Large":[0],"amounts":[1],"of":[2,54,62,76],"labeled":[3],"data":[4,20],"are":[5],"typically":[6],"required":[7],"to":[8],"train":[9],"deep":[10,30,82],"learning":[11,32,53,84],"models.":[12],"For":[13],"many":[14],"real-world":[15,77],"problems,":[16],"however,":[17],"acquiring":[18],"additional":[19],"can":[21],"be":[22],"expensive":[23],"or":[24],"even":[25],"impossible.":[26],"We":[27],"present":[28],"semi-supervised":[29,35,86],"kernel":[31,83],"(SSDKL),":[33],"a":[34,73],"regression":[36,78],"model":[37],"based":[38],"on":[39,72],"minimizing":[40],"predictive":[41],"variance":[42],"in":[43],"the":[44,50,58],"posterior":[45],"regularization":[46],"framework.":[47],"SSDKL":[48],"combines":[49],"hierarchical":[51],"representation":[52],"neural":[55],"networks":[56],"with":[57],"probabilistic":[59],"modeling":[60],"capabilities":[61],"Gaussian":[63],"processes.":[64],"By":[65],"leveraging":[66],"unlabeled":[67],"data,":[68],"we":[69],"show":[70],"improvements":[71],"diverse":[74],"set":[75],"tasks":[79],"over":[80],"supervised":[81],"and":[85,91],"methods":[87],"such":[88],"as":[89],"VAT":[90],"mean":[92],"teacher":[93],"adapted":[94],"for":[95],"regression.":[96]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2018-06-01T00:00:00"}
