Boosted Conformal Prediction Intervals

Ran Xie, Rina Foygel Barber, Emmanuel Candes

Keywords: Conformal Prediction, Uncertainty Quantification, (Other) Statistical Learning

TL;DR: We introduce a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length.

Abstract: This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length. We employ machine learning techniques, notably gradient boosting, to systematically improve upon a predefined conformity score function. This process is guided by carefully constructed loss functions that measure the deviation of prediction intervals from the targeted properties. The procedure operates post-training, relying solely on model predictions and without modifying the trained model (e.g., the deep network). Systematic experiments demonstrate that starting from conventional conformal methods, our boosted procedure achieves substantial improvements in reducing interval length and decreasing deviation from target conditional coverage.

Primary Area: Other (please use sparingly, only use the keyword field for more details)

Submission Number: 11953