The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

The existing methods of predicting with confidence give good accuracy and confidence
values, but quite often are computationally inefficient. Some partial solutions have been …

Traditional machine learning algorithms for pattern recognition just output simple
predictions, without any associated confidence values. Confidence values are an indication …

We study three notions of uncertainty quantification---calibration, confidence intervals and
prediction sets---for binary classification in the distribution-free setting, that is without making …

As network attacks have increased in number and severity over the past few years, intrusion
detection is increasingly becoming a critical component of secure information systems and …

In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-
NNR) algorithm and propose ways of extending the typical nonconformity measure used for …

This paper motivates and describes a novel realization of transductive inference that can
address the open set face recognition task. Open set operates under the assumption that not …

Gaussian Process Regression (GPR) is a popular regression method, which unlike most
Machine Learning techniques, provides estimates of uncertainty for its predictions. These …

Much of the existing face recognition systems operate in 2D. As the search for improved
recognition performance intensifies, alternative methods, eg, 3D, are under consideration …

In traditional graph learning tasks, such as node classification, learning is carried out in a
closed-world setting where the number of classes and their training samples are provided to …