2.1k citations · 3.1k across the 27 of their papers we have counts for
16 papers · 1 filter
Correcting the Coverage Bias of Quantile Regression
Isaac Gibbs, John J. Cherian, Emmanuel J. Candès
We develop a collection of methods for adjusting the predictions of quantile regression to ensure coverage. Our methods are model agnostic and can be used to correct for high-dimen…
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…
Derandomizing Knockoffs
Zhimei Ren, Yuting Wei, Emmanuel Candès
Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously c…
Classification with Valid and Adaptive Coverage
Yaniv Romano, Matteo Sesia, Emmanuel J. Candès
Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm to construct prediction sets with…
Knockoffs with Side Information
Zhimei Ren, Emmanuel Candès
We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available. In principle, using side information can all…
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…