通过仿冒过滤器进行差异私有变量选择

2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2021-09-12 DOI:10.1109/mlsp52302.2021.9596470

Mehrdad Pournaderi, Yu Xiang

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引用次数: 2

摘要

仿冒过滤器是Barber和candires最近开发的一种有效的程序，可以在控制错误发现率(FDR)的情况下进行变量选择。我们通过结合高斯和拉普拉斯机制提出了一个私有版本的仿造滤波器，并表明可以实现具有控制FDR的变量选择。仿真结果表明，我们的设置具有合理的统计能力。

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Differentially Private Variable Selection via the Knockoff Filter

The knockoff filter, recently developed by Barber and Candès, is an effective procedure to perform variable selection with a controlled false discovery rate (FDR). We propose a private version of the knockoff filter by incorporating Gaussian and Laplace mechanisms, and show that variable selection with controlled FDR can be achieved. Simulations demonstrate that our setting has reasonable statistical power.

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2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)

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