English(EN) A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

开发了新的差分隐私加权经验风险最小化算法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-24 04:00

研究人员开发了一种新的差分隐私加权经验风险最小化（wERM）算法，这是标准ERM的推广，它考虑了不同个体对目标函数的贡献。这种新颖的方法提供了正式的隐私保证，并推导了经验和总体超额风险界限。该框架特别适用于个体化治疗规则的隐私保护方法，例如结果加权学习（OWL），并在模拟和真实数据实验中展示了稳健的性能。 AI

影响增强了在敏感数据上训练的机器学习模型的隐私保证，从而能够更广泛地应用结果加权学习。

排序理由学术论文，详细介绍了新的差分隐私加权经验风险最小化算法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Spencer Giddens, Yiwang Zhou, Kevin R. Krull, Tara M. Brinkman, Peter X. K. Song, Fang Liu · 2026-06-24 04:00

一种差分隐私加权经验风险最小化方法及其在结果加权学习中的应用

arXiv:2307.13127v3 Announce Type: replace Abstract: Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically provable bounds to protect such data, previous work…

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一种差分隐私加权经验风险最小化方法及其在结果加权学习中的应用

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