Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules. - Johannes Rabold; Michael Siebers; Ute Schmid (ILP, 2018)

Enriching Visual with Verbal Explanations for Relational Concepts - Combining LIME with Aleph. - Johannes Rabold; Hannah Deininger; Michael Siebers; Ute Schmid (PKDD/ECML Workshops (1), 2019)

Effect of Superpixel Aggregation on Explanations in LIME - A Case Study with Biological Data. - Ludwig Schallner; Johannes Rabold; Oliver Scholz; Ute Schmid (PKDD/ECML Workshops (1), 2019)

Expressive Explanations of DNNs by Combining Concept Analysis with ILP. - Johannes Rabold; Gesina Schwalbe; Ute Schmid (KI, 2020)

A Neural-Symbolic Approach for Explanation Generation Based on Sub-concept Detection: An Application of Metric Learning for Low-Time-Budget Labeling. - Johannes Rabold (Künstliche Intell., 2022)

Generating contrastive explanations for inductive logic programming based on a near miss approach. - Johannes Rabold; Michael Siebers; Ute Schmid (Mach. Learn., 2022)

When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX. - Bettina Finzel; Patrick Hilme; Johannes Rabold; Ute Schmid (CoRR, 2024)