Arrow Research search
Back to NeurIPS

NeurIPS 2019

Private Learning Implies Online Learning: An Efficient Reduction

Conference Paper Artificial Intelligence ยท Machine Learning

Abstract

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.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
641242811803994971
v2026.09.13