Adaptive Batch Size for Privately Finding Second-Order Stationary Points
<|reference_start|>Adaptive Batch Size for Privately Finding Second-Order Stationary Points: There is a gap between finding a first-order stationary point (FOSP) and a second-order stationary point (SOSP) under differential privacy constraints, and it remains unclear whether privately finding an SOSP is more challenging than finding an FOSP. Specifically, Ganesh et al. (2023) demonstrated that an $\alpha$-SOSP can be found with $\alpha=O(\frac{1}{n^{1/3}}+(\frac{\sqrt{d}}{n\epsilon})^{3/7})$, where $n$ is the dataset size, $d$ is the dimension, and $\epsilon$ is the differential privacy parameter. Building on the SpiderBoost algorithm framework, we propose a new approach that uses adaptive batch sizes and incorporates the binary tree mechanism. Our method improves the results for privately finding an SOSP, achieving $\alpha=O(\frac{1}{n^{1/3}}+(\frac{\sqrt{d}}{n\epsilon})^{1/2})$. This improved bound matches the state-of-the-art for finding an FOSP, suggesting that privately finding an SOSP may be achievable at no additional cost.<|reference_end|>
@article{liu2024adaptive,
    title={Adaptive Batch Size for Privately Finding Second-Order Stationary Points},
    author={Daogao Liu, Kunal Talwar},
    journal={arXiv preprint arXiv:2410.07502},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07502},
    primaryClass={cs.LG cs.CR cs.DS stat.ML}
}