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Research Paper Analyzes Privacy's Impact on CVaR Learning

作者 PulseAugur 编辑部 · [2 个来源] ·

A new research paper explores how differential privacy impacts the learning of Conditional Value at Risk (CVaR). The study reveals that privacy mechanisms alter the effective sample size for CVaR calculations, with the privacy-relevant sample size being proportional to the tail mass. This leads to a decomposition of CVaR excess risk into statistical error and a privacy cost, with specific rates identified for scalar estimation and finite classes. AI

影响 Identifies a theoretical limitation in applying differential privacy to tail-risk learning, potentially impacting the development of robust and private financial AI models.

排序理由 The cluster contains a research paper published on arXiv detailing theoretical findings in machine learning.

在 arXiv cs.LG 阅读 →

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Research Paper Analyzes Privacy's Impact on CVaR Learning

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · El Mustapha Mansouri ·

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

    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…

  2. arXiv stat.ML TIER_1 English(EN) · El Mustapha Mansouri ·

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

    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…

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