Private Online Learning via Lazy Algorithms

arXiv:2406.03620v1 Announce Type: cross
Abstract: We study the problem of private online learning, specifically, online prediction from experts (OPE) and online convex optimization (OCO). We propose a new transformation that transforms lazy online learning algorithms into private algorithms. We apply our transformation for differentially private OPE and OCO using existing lazy algorithms for these problems. Our final algorithms obtain regret, which significantly improves the regret in the high privacy regime $\varepsilon \ll 1$, obtaining $\sqrt{T \log d} + T^{1/3} \log(d)/\varepsilon^{2/3}$ …

algorithms arxiv cs.cr cs.ds cs.lg experts math.oc online learning optimization prediction private problem stat.ml study transformation using