Machines Explaining Linear Programs. - David Steinmann; Matej Zecevic; Devendra Singh Dhami; Kristian Kersting (CoRR, 2022)

One Explanation Does Not Fit XIL. - Felix Friedrich; David Steinmann; Kristian Kersting (Tiny Papers @ ICLR, 2023)

Learning by Self-Explaining. - Wolfgang Stammer; Felix Friedrich; David Steinmann; Hikaru Shindo; Kristian Kersting (CoRR, 2023)

Learning to Intervene on Concept Bottlenecks. - David Steinmann; Wolfgang Stammer; Felix Friedrich; Kristian Kersting (ICML, 2024)

Neural Concept Binder. - Wolfgang Stammer; Antonia Wüst; David Steinmann; Kristian Kersting (NeurIPS, 2024)

Right on Time: Revising Time Series Models by Constraining their Explanations. - Maurice Kraus; David Steinmann; Antonia Wüst; Andre Kokozinski; Kristian Kersting (CoRR, 2024)

United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once. - Maurice Kraus; Felix Divo; David Steinmann; Devendra Singh Dhami; Kristian Kersting (CoRR, 2024)

Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation. - David Steinmann; Felix Divo; Maurice Kraus; Antonia Wüst; Lukas Struppek; Felix Friedrich; Kristian Kersting (CoRR, 2024)

Learning by Self-Explaining. - Wolfgang Stammer; Felix Friedrich; David Steinmann; Manuel Brack; Hikaru Shindo; Kristian Kersting (Trans. Mach. Learn. Res., 2024)

Object Centric Concept Bottlenecks. - David Steinmann; Wolfgang Stammer; Antonia Wüst; Kristian Kersting (CoRR, 2025)

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings. - Berkant Turan; Suhrab Asadulla; David Steinmann; Wolfgang Stammer; Sebastian Pokutta (CoRR, 2025)