4 papers
Posterior Conformal Prediction
Yao Zhang, Emmanuel J. Candès
Conformal prediction is a popular technique for constructing prediction intervals with distribution-free coverage guarantees. The coverage is marginal, meaning it only holds on ave…
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…
RandALO: Out-of-sample risk estimation in no time flat
Parth Nobel, Daniel LeJeune, Emmanuel J. Candès
Estimating out-of-sample risk for models trained on large high-dimensional datasets is an expensive but essential part of the machine learning process, enabling practitioners to op…
Characterizing the Training-Conditional Coverage of Full Conformal Inference in High Dimensions
Isaac Gibbs, Emmanuel J. Candès
We study the coverage properties of full conformal regression in the proportional asymptotic regime where the ratio of the dimension and the sample size converges to a constant. In…