Full Professor, Forschungszentrum Juelich GmbH
3 papers at NeurIPS 2025
We prove clean optimality results for clustering in ultrametrics, identify ways to take advantage of this theory and thoroughly evaluate the resulting techniques.
LeapFactual is a model-agnostic algorithm that generates reliable, actionable counterfactual explanations using conditional flow matching, overcoming key limitations of existing methods in high-stakes and human-in-the-loop domains.
In this work, we propose MIX (Multi-view Interactive Explanation), a novel framework that help to explain deep learning models in a multi-view setting.