Web: http://arxiv.org/abs/2206.08111

June 17, 2022, 1:10 a.m. | Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao, Jiheng Zhang

cs.LG updates on arXiv.org arxiv.org

Differentially private (DP) stochastic convex optimization (SCO) is
ubiquitous in trustworthy machine learning algorithm design. This paper studies
the DP-SCO problem with streaming data sampled from a distribution and arrives
sequentially. We also consider the continual release model where parameters
related to private information are updated and released upon each new data,
often known as the online algorithms. Despite that numerous algorithms have
been developed to achieve the optimal excess risks in different $\ell_p$ norm
geometries, yet none of the …

algorithms arxiv geometry lg on online optimization

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