3 papers
stat.ME2025
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…
math.ST2025
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…
stat.ML2024
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…