arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.09269math.STstat.APstat.MEstat.MLstat.TH
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多个仿制品的聚合

Aggregation of Multiple Knockoffs

Tuan-Binh Nguyen, Jérôme-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot

备注：
Accepted to ICML 2020 (Thirty-seventh International Conference on Machine Learning). This version includes both the main text of the conference paper and supplementary materials (as appendices). 35 pages, 7 figures
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摘要：

我们开发了Barber和Candes（2015）引入的Knockoff推理过程的扩展。这种新方法被称为多个敲除物的聚合（AKO），解决了基于敲除物的推断的随机性质所固有的不稳定性。具体而言，与原始Knockoff算法相比，AKO提高了稳定性和功率，同时仍然保持对错误发现率控制的保证。我们提供了一个新的推理过程，证明了它的核心属性，并在一组合成和真实数据集上的实验中证明了它的好处。

英文摘要：

We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015). This new method, called Aggregation of Multiple Knockoffs (AKO), addresses the instability inherent to the random nature of Knockoff-based inference. Specifically, AKO improves both the stability and power compared with the original Knockoff algorithm while still maintaining guarantees for False Discovery Rate control. We provide a new inference procedure, prove its core properties, and demonstrate its benefits in a set of experiments on synthetic and real datasets.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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