Conformal methods create prediction bands that control average coverage assuming solely
iid data. Although the literature has mostly focused on prediction intervals, more general …

Conformal prediction is a powerful framework for constructing prediction sets with valid
coverage guarantees in multi-class classification. However, existing methods often rely on a …

We develop a method to generate prediction sets with a guaranteed coverage rate that is
robust to corruptions in the training data, such as missing or noisy variables. Our approach …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

We propose a new method called localized conformal prediction, where we can perform
conformal inference using only a local region around a new test sample to construct its …

The property of conformal predictors to guarantee the required accuracy rate makes this
framework attractive in various practical applications. However, this property is achieved at a …

Conformal prediction is a simple and powerful tool that can quantify uncertainty without any
distributional assumptions. Many existing methods only address the average coverage …

Conformal prediction has been a popular distribution-free framework for uncertainty
quantification. In this paper, we present a novel conformal prediction method for time-series …

This paper introduces a boosted conformal procedure designed to tailor conformalized
prediction intervals toward specific desired properties, such as enhanced conditional …

Conformal regression provides prediction intervals with global coverage guarantees, but
often fails to capture local error distributions, leading to non-homogeneous coverage. We …