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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…
With Malice Towards None: Assessing Uncertainty via Equalized Coverage
Yaniv Romano, Rina Foygel Barber, Chiara Sabatti +1
An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers. This can be accompl…
Conformalized Quantile Regression
Yaniv Romano, Evan Patterson, Emmanuel J. Candès
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions. Despite this appe…
Predictive inference with the jackknife+
Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas +1
This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted…
Conformal Prediction Under Covariate Shift
Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candes +1
We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distr…
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…