Variable Selection with Knockoffs

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants

Adjusting the Benjamini-Hochberg method for controlling the false discovery rate in knockoff assisted variable selection

ROC performance of combined, morphological, and texturefeatures with

A Powerful and Precise Feature-level Filter using Group Knockoffs

NeurIPS 2022

Seminar University of Washington Department of Statistics

2022 – 2023 Acad. Year UCLA Statistics & Data Science

IPI PAN ZBO

Controlling the False Discovery Rate via Knockoff for High Dimensional Ising Model Variable Selection

Power and FDR in simulations involving samples with diverse ancestries.

Identification of putative causal loci in whole-genome sequencing data via knockoff statistics