2000 character limit reached
Differentially Private Variable Selection via the Knockoff Filter
Published 12 Sep 2021 in stat.ML, cs.CR, cs.DB, cs.IT, cs.LG, and math.IT | (2109.05402v3)
Abstract: 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.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.