GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. - Patrick Zschech; Sven Weinzierl; Nico Hambauer; Sandra Zilker; Mathias Kraus (ECIS, 2022)

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. - Maximilian V. Harl; Marvin Herchenbach; Sven Kruschel; Nico Hambauer; Patrick Zschech; Mathias Kraus (Wirtschaftsinformatik, 2022)

IGANN Sparse: Bridging Sparsity and Interpretability with Non-Linear Insight. - Theodor Felix Stoecker; Nico Hambauer; Patrick Zschech; Mathias Kraus (ECIS, 2024)

Coupling Neural Networks Between Clusters for Better Personalized Care. - Mathias Kraus; Nico Hambauer; Kristina Müller; Pavlina Kröckel; Nalika Ulapane; Arno De Caigny; Koen W. De Bock; Kristof Coussement (HICSS, 2024)

Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. - Sven Kruschel; Nico Hambauer; Sven Weinzierl; Sandra Zilker; Mathias Kraus; Patrick Zschech (CoRR, 2024)