Methods to classify objects into two or more classes are at the core of various disciplines.
When a set of objects with their true classes is available, a supervised classifier can be …

Typically, point forecasting methods are compared and assessed by means of an error
measure or scoring function, with the absolute error and the squared error being key …

Generative adversarial networks (GANs) provide an algorithmic framework for constructing
generative models with several appealing properties: they do not require a likelihood …

This paper studies the problem of post-hoc calibration of machine learning classifiers. We
introduce the following desiderata for uncertainty calibration:(a) accuracy-preserving,(b) …

Abstract Out-of-distribution (OOD) detection has been extensively studied in order to
successfully deploy neural networks, in particular, for safety-critical applications. Moreover …

Automatic localization of defects in metal castings is a challenging task, owing to the rare
occurrence and variation in appearance of defects. Convolutional neural networks (CNN) …

Analysis of non-stationary signals in a noisy environment is a challenging research topic in
many fields often requiring simultaneous signal decomposition in the time and frequency …

Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score
based on the predictive distribution and on the event or value that materializes. A scoring …

Performance metrics in classification are fundamental in assessing the quality of learning
methods and learned models. However, many different measures have been defined in the …

We present a statistical perspective on boosting. Special emphasis is given to estimating
potentially complex parametric or nonparametric models, including generalized linear and …