Conformal prediction (CP) is an algorithm for uncertainty quantification that produces statistically valid prediction regions (multidimensional prediction intervals) for any underlying point predictor (whether …

Jul 15, 2021 · This hands-on introduction is aimed to provide the reader a working understanding of conformal prediction and related distribution-free uncertainty quantification techniques with one self …

Conformal prediction is a relatively new framework for quantifying uncertainty in the predictions made by arbitrary prediction algorithms. Fundamentally, it does so by converting an algorithm’s predictions …

Jul 23, 2025 · Conformal Inference is a flexible model that can be applied to any black-box predictor and what makes it more useful is its ability to produce prediction sets or intervals that come with rigorous …

Mar 18, 2025 · This tutorial dives into conformal prediction, covering basics, advanced methods with Python code, and resources to help you apply CP in your next project!

This tutorial presents a self-contained account of the theory of conformal prediction and works through several numerical examples. A more comprehensive treatment of the topic is provided in Algorithmic …

Nov 24, 2023 · What is Conformal Prediction? Conformal prediction is both a method of uncertainty quantification, and a method of classifying instances (which may be fine-tuned for classes or …

Probabilities of complex outputs quickly become small Probabilities of different portions of the output can be highly correlated Conformal prediction Represents of uncertainty using prediction sets, which can …

Nov 18, 2024 · This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, …

The approach we consider in these notes is conformal prediction. The idea is due to Vovk, Gammerman and Shafer (2005). The statistical theory for conformal prediction was devel-oped in Lei, Robins and …