Conformal prediction (CP) is a learning framework controlling prediction coverage of
prediction sets, which can be built on any learning algorithm for point prediction. This work …

We propose a learning framework for calibrating predictive models to make loss-controlling
prediction for exchangeable data, which extends our recently proposed conformal loss …

Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a
conventional point predictor to provide calibration guarantees. Generalizing conformal …

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 …

In this work, we consider the problem of building distribution-free prediction intervals with
finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increasingly …

The safe integration of machine learning modules in decision-making processes hinges on
their ability to quantify uncertainty. A popular technique to achieve this goal is conformal …

In this paper, we focus on the problem of conformal prediction with conditional guarantees.
Prior work has shown that it is impossible to construct nontrivial prediction sets with full …

Modern black-box predictive models are often accompanied by weak performance
guarantees that only hold asymptotically in the size of the dataset or require strong …

Distribution regression is the regression case where the input objects are distributions. Many
machine learning problems can be analyzed in this framework, such as multi-instance …

In a supervised learning problem, given a predicted value that is the output of some trained
model, how can we quantify our uncertainty around this prediction? Distribution-free …