R Luo, Z Zhou - arXiv preprint arXiv:2407.10230, 2024 - arxiv.org
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 …
S Feldman, Y Romano - arXiv preprint arXiv:2406.05405, 2024 - arxiv.org
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 …
L Guan - arXiv preprint arXiv:1908.08558, 2019 - arxiv.org
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 …
J Lee, C Xu, Y Xie - arXiv preprint arXiv:2405.16828, 2024 - arxiv.org
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 …
R Xie, RF Barber, EJ Candès - arXiv preprint arXiv:2406.07449, 2024 - arxiv.org
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 …