Classical probability is not the only mathematical theory of uncertainty, or the most general.
Many authors have argued that probability theory is ill-equipped to model the 'epistemic' …

Dealing with uncertain data in statistical estimation problems or in machine learning is not
really a new issue. However, such uncertainty has so far mostly been modelled either as …

Self-training is a simple yet effective method within semi-supervised learning. Self-training's
rationale is to iteratively enhance training data by adding pseudo-labeled data. Its …

The last decade in deep learning has brought on increasingly capable systems that are
deployed on a wide variety of applications. In natural language processing, the field has …

In this article we study the problem of credal learning, a general form of weakly supervised
learning in which instances are associated with credal sets (ie, closed, convex sets of …

Self-training is a simple yet effective method within semi-supervised learning. The idea is to
iteratively enhance training data by adding pseudo-labeled data. Its generalization …

We consider the problem of supervised classification. We focus on the problem of calibrating
the classifier's outputs. We show that the p-values provided by Inductive Conformal …

Machine learning has made tremendous strides in the past decades at producing state-of-
the-art results in safety-critical fields such as self-driving vehicles and medicine. Current …

Automated Machine Learning (AutoML) aims to automatically select and configure machine
learning algorithms for optimal performance on given datasets. In real-world applications …