8 papers
Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection
Rodrigo F. L. Lassance, Jasper De Bock
Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how…
Computing lower expectations with respect to total variation distance and chi-squared divergence balls
Jasper De Bock
We derive closed form expressions for the lower expectations that correspond to total variation distance and chi-squared divergence balls around a probability mass function over a…
A decision-theoretic approach to dealing with uncertainty in quantum mechanics
Keano De Vos, Gert de Cooman, Alexander Erreygers +1
We provide a decision-theoretic framework for dealing with uncertainty in quantum mechanics. This uncertainty is two-fold: on the one hand there may be uncertainty about the state…
Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions
Adrián Detavernier, Jasper De Bock
We consider two approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We expla…
Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier
Adrián Detavernier, Jasper De Bock
We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quanti…
Robustness quantification: a new method for assessing the reliability of the predictions of a classifier
Adrián Detavernier, Jasper De Bock
Based on existing ideas in the field of imprecise probabilities, we present a new approach for assessing the reliability of the individual predictions of a generative probabilistic…