Robust inference with the knockoff filter

In this talk, I will present ongoing work on the knockoff filter for inference in regression. In a high-dimensional model selection problem, we would like to select relevant features without too many false positives. The knockoff filter provides a tool for model selection by creating knockoff copies of each feature, testing the model selection algorithm for its ability to distinguish true from false covariates to control the false positives. In practice, the modeling assumptions that underlie the construction of the knockoffs may be violated. Our ongoing work aims to determine and improve the robustness properties of the knockoff framework. This work is joint with Emmanuel Candès and Richard Samworth.