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Private Learning Implies Online Learning: An Efficient Reduction
Alon Gonen · Elad Hazan · Shay Moran
We 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.
Author Information
Alon Gonen (UCSD)
Elad Hazan (Princeton University)
Shay Moran (Google AI Princeton)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Private Learning Implies Online Learning: An Efficient Reduction »
Fri. Dec 13th 01:00 -- 03:00 AM Room East Exhibition Hall B + C #24
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