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Private Learning Implies Online Learning: An Efficient Reduction

Author(s): Gonen, Alon; Hazan, Elad; Moran, Shay

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dc.contributor.authorGonen, Alon-
dc.contributor.authorHazan, Elad-
dc.contributor.authorMoran, Shay-
dc.date.accessioned2021-10-08T19:49:50Z-
dc.date.available2021-10-08T19:49:50Z-
dc.date.issued2019en_US
dc.identifier.citationGonen, Alon, Elad Hazan, and Shay Moran. "Private Learning Implies Online Learning: An Efficient Reduction." Advances in Neural Information Processing Systems 32 (2019).en_US
dc.identifier.issn1049-5258-
dc.identifier.urihttps://papers.nips.cc/paper/2019/file/700fdb2ba62d4554dc268c65add4b16e-Paper.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1nk14-
dc.description.abstractWe study the relationship between the notions of differentially private learning and online learning. Several recent works have shown that differentially private learning implies online learning, but an open problem of Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is {\it efficient}. Specifically, does an efficient differentially private learner imply an efficient online learner? In this paper we resolve this open question in the context of pure differential privacy. We derive an efficient black-box reduction from differentially private learning to online learning from expert advice.en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titlePrivate Learning Implies Online Learning: An Efficient Reductionen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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