148 citations · 405 across the 8 of their papers we have counts for
8 papers
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…
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…
Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets
Charmaine Chia, Matteo Sesia, Chi-Sing Ho +4
Deep neural networks and other sophisticated machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate pred…
Causal Inference in Genetic Trio Studies
Stephen Bates, Matteo Sesia, Chiara Sabatti +1
We introduce a method to rigorously draw causal inferences---inferences immune to all possible confounding---from genetic data that include parents and offspring. Causal conclusion…
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…
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…
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…
Gene Hunting with Knockoffs for Hidden Markov Models
Matteo Sesia, Chiara Sabatti, Emmanuel J. Candès
Modern scientific studies often require the identification of a subset of relevant explanatory variables, in the attempt to understand an interesting phenomenon. Several statistica…