3 papers
cs.HC2026
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
Tabea E. Röber, Paul Festor, Rob Goedhart +2
Experimental user studies evaluating the effectiveness of different subtypes of post-hoc explanations for black-box models are largely nonexistent. Therefore, the aim of this study…
math.OC2026
Linear Model Extraction via Factual and Counterfactual Queries
Daan Otto, Jannis Kurtz, Dick den Hertog +1
In model extraction attacks, the goal is to reveal the parameters of a black-box machine learning model by querying the model for a selected set of data points. Due to an increasin…
stat.AP2024
Modeling Alzheimer's Disease: Bayesian Copula Graphical Model from Demographic, Cognitive, and Neuroimaging Data
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +2
The early detection of Alzheimer's disease (AD) requires an understanding of the relationships between a wide range of features. Conditional independencies and partial correlations…