English(EN) The Privacy Price of Tail-Risk Learning: Effective Tail Sample Size in Differentially Private CVaR Optimization

研究论文分析隐私对CVaR学习的影响

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-15 17:30

一篇新的研究论文探讨了差分隐私如何影响条件在险价值（CVaR）的学习。研究表明，隐私机制改变了CVaR计算的有效样本量，隐私相关的样本量与尾部质量成正比。这导致CVaR超额风险分解为统计误差和隐私成本，并为标量估计和有限类确定了特定速率。 AI

影响识别出将差分隐私应用于尾部风险学习中的理论局限性，可能影响稳健且私有的金融AI模型的开发。

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了机器学习的理论发现。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · El Mustapha Mansouri · 2026-05-15 17:30

差分隐私 CVaR 优化中尾部风险学习的隐私成本：有效的尾部样本量

Differential privacy changes the effective sample size governing CVaR learning. For tail mass $τ$, the privacy-relevant sample size is not $n$, but $nτ$; equivalently, the effective private tail sample size is $εnτ$. Private CVaR excess risk decomposes into ordinary tail-risk sta…
arXiv stat.ML TIER_1 English(EN) · El Mustapha Mansouri · 2026-05-18 04:00

差分隐私CVaR优化中尾部风险学习的隐私成本：有效的尾部样本量

arXiv:2605.16219v1 Announce Type: cross Abstract: Differential privacy changes the effective sample size governing CVaR learning. For tail mass $\tau$, the privacy-relevant sample size is not $n$, but $n\tau$; equivalently, the effective private tail sample size is $\epsilon n\ta…