Conformal prediction is a distribution-free technique for establishing valid prediction
intervals. Although conventionally people conduct conformal prediction in the output space …

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 …

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 …