{"id":"https://openalex.org/W3133066671","doi":null,"title":"Learner-Private Online Convex Optimization.","display_name":"Learner-Private Online Convex Optimization.","publication_year":2021,"publication_date":"2021-02-23","ids":{"openalex":"https://openalex.org/W3133066671","mag":"3133066671"},"language":"en","primary_location":{"id":"mag:3133066671","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2102.11976","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},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2102.11976","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101746186","display_name":"Jiaming Xu","orcid":"https://orcid.org/0000-0001-6104-4742","observed_orcids":["https://orcid.org/0000-0001-6104-4742"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaming Xu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-6104-4742","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007261929","display_name":"Kuang Xu","orcid":"https://orcid.org/0000-0002-2221-1648","observed_orcids":["https://orcid.org/0000-0002-2221-1648"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuang Xu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2221-1648","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055643323","display_name":"Dana Yang","orcid":"https://orcid.org/0000-0001-9460-569X","observed_orcids":["https://orcid.org/0000-0001-9460-569X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dana Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9789999723434448,"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":0.9789999723434448,"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":0.00989999994635582,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.004699999932199717,"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/online-convex-optimization","display_name":"online convex optimization","score":0.9729999899864197},{"id":"https://openalex.org/keywords/student-privacy","display_name":"student privacy","score":0.9240000247955322},{"id":"https://openalex.org/keywords/query-complexity","display_name":"query complexity","score":0.9120000004768372},{"id":"https://openalex.org/keywords/information-leakage","display_name":"information leakage","score":0.8180000185966492},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.9120000004768372},{"id":"https://openalex.org/keywords/sequential-learning","display_name":"sequential learning","score":0.460999995470047}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6211260557174683},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.5944448113441467},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.5822252035140991},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.5208573937416077},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.467633992433548},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4599006175994873},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4542475640773773},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.42895370721817017},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.363578736782074},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.3410690128803253},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2867690622806549},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26011261343955994},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3133066671","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2102.11976","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}],"best_oa_location":{"id":"mag:3133066671","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2102.11976","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},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2033092527","https://openalex.org/W2055233491","https://openalex.org/W2091768257","https://openalex.org/W2164278908","https://openalex.org/W2891852340","https://openalex.org/W2963536309","https://openalex.org/W2969695741","https://openalex.org/W2995022099","https://openalex.org/W3035408628","https://openalex.org/W3049605332","https://openalex.org/W3105568421","https://openalex.org/W3114647763","https://openalex.org/W3152187716","https://openalex.org/W3212016815"],"related_works":["https://openalex.org/W3169557808","https://openalex.org/W3105568421","https://openalex.org/W2979864439","https://openalex.org/W3049605332","https://openalex.org/W7097259305","https://openalex.org/W2973639764","https://openalex.org/W7097884644","https://openalex.org/W2963536309","https://openalex.org/W2964064173","https://openalex.org/W3034638571"],"abstract_inverted_index":{"Online":[0],"convex":[1,25,77,107,199],"optimization":[2,40],"is":[3,85,109,120,128,163],"a":[4,7,24,101,113,133,234],"framework":[5],"where":[6],"learner":[8,50,99,141],"sequentially":[9],"queries":[10,73],"an":[11],"external":[12],"data":[13],"source":[14],"in":[15,38,74,104,116,160,165,167,173,175],"order":[16],"to":[17,35,51,68,88,132,143,180,192],"arrive":[18],"at":[19,226],"the":[20,49,59,71,91,106,118,123,140,145,151,158,168,176,193,212,228],"optimal":[21,83],"solution":[22],"of":[23,98,126,147,197,214,223,230],"function.":[26],"The":[27,44],"paradigm":[28],"has":[29],"gained":[30],"significant":[31],"popularity":[32],"recently":[33],"thanks":[34],"its":[36],"scalability":[37],"large-scale":[39],"and":[41,112,122],"machine":[42],"learning.":[43],"repeated":[45],"interactions,":[46],"however,":[47],"expose":[48],"privacy":[52],"risks":[53],"from":[54,211],"eavesdropping":[55,92],"adversary":[56,152],"that":[57,80],"observe":[58],"submitted":[60],"queries.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"study":[66],"how":[67],"optimally":[69],"obfuscate":[70],"learner's":[72],"first-order":[75],"online":[76],"optimization,":[78],"so":[79],"their":[81],"learned":[82],"value":[84],"provably":[86],"difficult":[87],"estimate":[89],"for":[90],"adversary.":[93],"We":[94,136],"consider":[95],"two":[96],"formulations":[97],"privacy:":[100],"Bayesian":[102,177],"formulation":[103,115],"which":[105,117],"function":[108,119],"drawn":[110],"randomly,":[111],"minimax":[114,134,169],"fixed":[121],"adversary's":[124],"probability":[125,146],"error":[127],"measured":[129],"with":[130,186,201],"respect":[131],"criterion.":[135],"show":[137],"that,":[138],"if":[139],"wants":[142],"ensure":[144],"accurate":[148],"prediction":[149],"by":[150,209],"be":[153],"kept":[154],"below":[155],"$1/L$,":[156],"then":[157],"overhead":[159],"query":[161],"complexity":[162],"additive":[164],"$L$":[166,174],"formulation,":[170],"but":[171],"multiplicative":[172],"formulation.":[178],"Compared":[179],"existing":[181],"learner-private":[182],"sequential":[183],"learning":[184],"models":[185],"binary":[187],"feedback,":[188],"our":[189],"results":[190],"apply":[191],"significantly":[194],"richer":[195],"family":[196],"general":[198],"functions":[200],"full-gradient":[202,235],"feedback.":[203],"Our":[204],"proofs":[205],"are":[206],"largely":[207],"enabled":[208],"tools":[210],"theory":[213],"Dirichlet":[215],"processes,":[216],"as":[217,219],"well":[218],"more":[220],"sophisticated":[221],"lines":[222],"analysis":[224],"aimed":[225],"measuring":[227],"amount":[229],"information":[231],"leakage":[232],"under":[233],"oracle.":[236]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2021-03-01T00:00:00"}