Differentially Private Variable Selection via the Knockoff Filter
Not on the map. Why?
Abstract (arXiv)
The knockoff filter, recently developed by Barber and Candes, 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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 12 Sep 2021.
Builds on
7 of 17 references.
References · 7
- 2014Controlling the false discovery rate via knockoffscited by 97
- 2018DeepPINK: reproducible feature selection in deep neural networkscited by 41
- 2018Deep Knockoffscited by 41
- 2015Differentially Private Ordinary Least Squarescited by 36
- 2016A knockoff filter for high-dimensional selective inferencecited by 25
- 2018Differentially Private False Discovery Rate Controlcited by 15
- 2018IPAD: Stable Interpretable Forecasting with Knockoffs Inferencecited by 5
Built on by
Citing papers per year: 2022: 1.
1 paper.