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cs.LG2026
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):…
math.PR2026
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…
cs.LG2026
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…