activity
20182021
most citedRejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"

18 citations · 26 across the 3 of their papers we have counts for

collaborators

5 papers

stat.AP20211 cited

Transfer learning in genome-wide association studies with knockoffs

Shuangning Li, Zhimei Ren, Chiara Sabatti +1

This paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external…

stat.ME20217 cited

Searching for consistent associations with a multi-environment knockoff filter

Shuangning Li, Matteo Sesia, Yaniv Romano +2

This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate. The…

stat.ME2019

A comparison of some conformal quantile regression methods

Matteo Sesia, Emmanuel J. Candès

We compare two recently proposed methods that combine ideas from conformal inference and quantile regression to produce locally adaptive and marginally valid prediction intervals u…

stat.ME201918 cited

Rejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"

Matteo Sesia, Chiara Sabatti, Emmanuel J. Candès

In this paper we deepen and enlarge the reflection on the possible advantages of a knockoff approach to genome wide association studies (Sesia et al., 2018), starting from the disc…

stat.ME2018

Deep Knockoffs

Yaniv Romano, Matteo Sesia, Emmanuel J. Candès

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iterat…