新框架确立私有PAC学习的下界

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一个新框架，用于证明在近似差分隐私下可实现PAC学习的样本复杂度下界。该框架引入了独立类别打包（ICP）参数，该参数被证明可以表征某些数据类别的私有PAC样本复杂度。该研究为轴对齐矩形、区间并集和线性子空间提供了严格的下界，并为半空间和凸多边形提供了改进的界限。 AI

影响为隐私保护的机器学习算法确立了理论极限。

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arXiv cs.LG TIER_1 English(EN) · Prathamesh Dharangutte, Amartya Sanyal · 2026-11-02 05:00

New Lower Bounds for Private PAC Learning

arXiv:2610.41980v1 Announce Type: new Abstract: We study realizable PAC learning under approximate differential privacy, and give a framework for proving lower bounds on its sample complexity. We construct classes with identical VC and Littlestone dimensions but different private…