Conformal prediction methodologies have significantly advanced the quantification of uncertainties in predictive models. Yet, the construction of confidence regions for model …
While conformal predictors reap the benefits of rigorous statistical guarantees on their error frequency, the size of their corresponding prediction sets is critical to their practical utility …
We develop a new method for creating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution $ P_ {Y\mid X} $. Most …
In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction candidates--in place of a single prediction. This set is …
Conformal Prediction is a machine learning methodology that produces valid prediction regions under mild conditions. In this paper, we explore the application of making …
Conformal Predictive Systems (CPS) offer a versatile framework for constructing predictive distributions, allowing for calibrated inference and informative decision-making. However …
Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties for …
B LeRoy, D Zhao - arXiv preprint arXiv:2107.03280, 2021 - arxiv.org
Quantifying uncertainty in model predictions is a common goal for practitioners seeking more than just point predictions. One tool for uncertainty quantification that requires minimal …
We propose a new inference framework called localized conformal prediction. It generalizes the framework of conformal prediction by offering a single-test-sample adaptive construction …