Conformal Prediction (CP) algorithms estimate the uncertainty of a prediction model by
calibrating its outputs on labeled data. The same calibration scheme usually applies to any …

Conformal prediction presents a novel idea whose error rate is provably controlled by given
significant levels. So the remaining goal of conformal prediction is its efficiency. High …

Abstract Cross-Conformal Prediction (CCP) is a recently proposed approach for overcoming
the computational inefficiency problem of Conformal Prediction (CP) without sacrificing as …

Predictive inference under a general regression setting is gaining more interest in the big-
data era. In terms of going beyond point prediction to develop prediction intervals, two main …

Conformal prediction provides distribution-free uncertainty quantification under minimal
assumptions. An important ingredient in conformal prediction is the so-called nonconformity …

Conformal prediction, as an emerging uncertainty qualification technique, constructs
prediction sets that are guaranteed to contain the true label with high probability. Previous …

Conformal prediction methods construct prediction regions for iid data that are valid in finite
samples. We provide two parametric conformal prediction regions that are applicable for a …

Uncertainty estimates must be calibrated (ie, accurate) and sharp (ie, informative) in order to
be useful. This has motivated a variety of methods for recalibration, which use held-out data …

Training-conditional coverage guarantees in conformal prediction concern the concentration
of the error distribution, conditional on the training data, below some nominal level. The …

In most supervised classification and regression tasks, one would assume the provided
labels reflect the ground truth. In reality, this assumption is often violated; see Cheng et …