The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

Machine learning for malware classification shows encouraging results, but real
deployments suffer from performance degradation as malware authors adapt their …

Identifying spurious correlations learned by a trained model is at the core of refining a
trained model and building a trustworthy model. We present a simple method to identify …

Abstract Machine learning based decision making systems in safety critical areas place high
demands on the accuracy and generalization ability of the underlying model. A common …

Machine learning based decision making systems applied in safety critical areas require
reliable high certainty predictions. For this purpose, the system can be extended by an reject …

Conformal predictors are a family of predictive models that associate with each of their
predictions a measure of confidence, enabling them to provide quantitative information …

The application of machine learning based decision making systems in safety critical areas
requires reliable high certainty predictions. Reject options are a common way of ensuring a …

Iterative screening has emerged as a promising approach to increase the efficiency of high-
throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the …

Beyond the standard classification scenario, allowing a classifier to refrain from making a
prediction under uncertainty can have advantages in safety-critical applications, where a …

A standard classifier is forced to predict the label of every test instance, even when
confidence in the predictions is very low. In many scenarios, it would, however, be better to …