27 citations · 60 across the 7 of their papers we have counts for
7 papers
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…
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…
Large language model validity via enhanced conformal prediction methods
John J. Cherian, Isaac Gibbs, Emmanuel J. Candès
We develop new conformal inference methods for obtaining validity guarantees on the output of large language models (LLMs). Prior work in conformal language modeling identifies a s…
Conformal Prediction With Conditional Guarantees
Isaac Gibbs, John J. Cherian, Emmanuel J. Candès
We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact con…
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts
Isaac Gibbs, Emmanuel Candès
We consider the problem of forming prediction sets in an online setting where the distribution generating the data is allowed to vary over time. Previous approaches to this problem…
Adaptive Conformal Inference Under Distribution Shift
Isaac Gibbs, Emmanuel Candès
We develop methods for forming prediction sets in an online setting where the data generating distribution is allowed to vary over time in an unknown fashion. Our framework builds…
Asymptotic Properties of Random Voronoi Cells with Arbitrary Underlying Density
Isaac Gibbs, Linan Chen
We consider the Voronoi diagram generated by i.i.d. -valued random variables with an arbitrary underlying probability density function on ,…