4 papers
Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach
Adrián Detavernier, Jasper De Bock
We provide methods for calculating the robustness of the predictions of two types of generative classifiers whose underlying distribution is a Probabilistic Graphical Model (PGM):…
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…
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…
Extending choice assessments to choice functions: An algorithm for computing the natural extension
Arne Decadt, Alexander Erreygers, Jasper De Bock
We study how to infer new choices from prior choices using the framework of choice functions, a unifying mathematical framework for decision-making based on sets of preference orde…