{"id":"https://openalex.org/W3042010593","doi":"https://doi.org/10.24963/ijcai.2020/705","title":"Variational Bayes in Private Settings (VIPS) (Extended Abstract)","display_name":"Variational Bayes in Private Settings (VIPS) (Extended Abstract)","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3042010593","doi":"https://doi.org/10.24963/ijcai.2020/705","mag":"3042010593"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/705","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/705","pdf_url":"https://www.ijcai.org/proceedings/2020/0705.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0705.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010326834","display_name":"James R. Foulds","orcid":"https://orcid.org/0000-0003-0935-4182"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James R. Foulds","raw_affiliation_strings":["Department of Information Systems, University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Systems, University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101506370","display_name":"Mijung Park","orcid":"https://orcid.org/0000-0003-1771-6104"},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]},{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mijung Park","raw_affiliation_strings":["Department of Computer Science, University of Tubingen","Max Planck Institute for Intelligent Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tubingen","institution_ids":["https://openalex.org/I8087733"]},{"raw_affiliation_string":"Max Planck Institute for Intelligent Systems","institution_ids":["https://openalex.org/I4210135521"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010790447","display_name":"Kamalika Chaudhuri","orcid":"https://orcid.org/0000-0001-9646-7710"},"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":"Kamalika Chaudhuri","raw_affiliation_strings":["Department of Computer Science, University of California, San Diego"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087368991","display_name":"Max Welling","orcid":"https://orcid.org/0000-0003-1484-2121"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Max Welling","raw_affiliation_strings":["Amsterdam Machine Learning LAB (AMLAB), Informatics Institute, University of Amsterdam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amsterdam Machine Learning LAB (AMLAB), Informatics Institute, University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07018359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5050","last_page":"5054"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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/T11719","display_name":"Data Quality and Management","score":0.9740999937057495,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.947700023651123,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7345868349075317},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.6817315220832825},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.6761890053749084},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6068736910820007},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5513474941253662},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5371668338775635},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47619450092315674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4445202648639679},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4391267001628876},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4349588453769684},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.43289339542388916},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42886796593666077},{"id":"https://openalex.org/keywords/personally-identifiable-information","display_name":"Personally identifiable information","score":0.41548678278923035},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38755473494529724},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15836554765701294},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.10330721735954285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7345868349075317},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6817315220832825},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.6761890053749084},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6068736910820007},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5513474941253662},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5371668338775635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47619450092315674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4445202648639679},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4391267001628876},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4349588453769684},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.43289339542388916},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42886796593666077},{"id":"https://openalex.org/C169093310","wikidata":"https://www.wikidata.org/wiki/Q3702971","display_name":"Personally identifiable information","level":2,"score":0.41548678278923035},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38755473494529724},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15836554765701294},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.10330721735954285},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/705","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/705","pdf_url":"https://www.ijcai.org/proceedings/2020/0705.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/705","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/705","pdf_url":"https://www.ijcai.org/proceedings/2020/0705.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G5050400397","display_name":null,"funder_award_id":"ZUK 63","funder_id":"https://openalex.org/F4320321112","funder_display_name":"Eberhard Karls Universit\u00e4t T\u00fcbingen"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320321008","display_name":"Universiteit van Amsterdam","ror":"https://ror.org/04dkp9463"},{"id":"https://openalex.org/F4320321112","display_name":"Eberhard Karls Universit\u00e4t T\u00fcbingen","ror":"https://ror.org/03a1kwz48"},{"id":"https://openalex.org/F4320322434","display_name":"Max-Planck-Gesellschaft","ror":"https://ror.org/01hhn8329"},{"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/W3042010593.pdf","grobid_xml":"https://content.openalex.org/works/W3042010593.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W1557833142","https://openalex.org/W1566688548","https://openalex.org/W1873763122","https://openalex.org/W1880262756","https://openalex.org/W2027595342","https://openalex.org/W2060036647","https://openalex.org/W2115979064","https://openalex.org/W2121483459","https://openalex.org/W2122588206","https://openalex.org/W2165599843","https://openalex.org/W2473418344","https://openalex.org/W2547253982","https://openalex.org/W2626769593","https://openalex.org/W2963612912","https://openalex.org/W3021297499","https://openalex.org/W3037491300","https://openalex.org/W3139083889","https://openalex.org/W4231510805"],"related_works":["https://openalex.org/W4385957115","https://openalex.org/W2061372042","https://openalex.org/W3047779762","https://openalex.org/W1520030019","https://openalex.org/W3038283795","https://openalex.org/W2071654592","https://openalex.org/W2088157920","https://openalex.org/W2604501336","https://openalex.org/W4283785902","https://openalex.org/W2734500670"],"abstract_inverted_index":{"Many":[0],"applications":[1],"of":[2,53,64,90,104,126],"Bayesian":[3,38,129],"data":[4,95],"analysis":[5],"involve":[6],"sensitive":[7],"information":[8],"such":[9],"as":[10],"personal":[11],"documents":[12],"or":[13],"medical":[14],"records,":[15],"motivating":[16],"methods":[17],"which":[18],"ensure":[19,68],"that":[20],"privacy":[21,48,87],"is":[22],"protected.":[23],"We":[24,70,99],"introduce":[25],"a":[26,34,57,123],"general":[27],"privacy-preserving":[28],"framework":[29,42],"for":[30],"Variational":[31],"Bayes":[32,55],"(VB),":[33],"widely":[35],"used":[36],"optimization-based":[37],"inference":[39],"method.":[40],"Our":[41],"respects":[43],"differential":[44],"privacy,":[45],"the":[46,62,81,86,102,115],"gold-standard":[47],"criterion.":[49],"The":[50],"iterative":[51],"nature":[52],"variational":[54],"presents":[56],"challenge":[58],"since":[59],"iterations":[60],"increase":[61],"amount":[63],"noise":[65],"needed":[66],"to":[67,122],"privacy.":[69],"overcome":[71],"this":[72],"by":[73],"combining:":[74],"(1)":[75],"an":[76],"improved":[77],"composition":[78],"method,":[79],"called":[80],"moments":[82],"accountant,":[83],"and":[84,132],"(2)":[85],"amplification":[88],"effect":[89],"subsampling":[91],"mini-batches":[92],"from":[93],"large-scale":[94],"in":[96],"stochastic":[97],"learning.":[98],"empirically":[100],"demonstrate":[101],"effectiveness":[103],"our":[105,120],"method":[106,121],"on":[107,112],"LDA":[108],"topic":[109],"models,":[110,127],"evaluated":[111],"Wikipedia.":[113],"In":[114],"full":[116],"paper":[117],"we":[118],"extend":[119],"broad":[124],"class":[125],"including":[128],"logistic":[130],"regression":[131],"sigmoid":[133],"belief":[134],"networks.":[135]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
