General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (xxAI@ICML, 2020)

Pitfalls to Avoid when Interpreting Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (CoRR, 2020)

Explaining Hyperparameter Optimization via Partial Dependence Plots. - Julia Moosbauer; Julia Herbinger; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (NeurIPS, 2021)

REPID: Regional Effect Plots with implicit Interaction Detection. - Julia Herbinger; Bernd Bischl; Giuseppe Casalicchio (AISTATS, 2022)

Portfolio optimization with optimal expected utility risk measures. - Sebastian Geissel; Holger Graf; Julia Herbinger; Frank Thomas Seifried (Ann. Oper. Res., 2022)

Grouped feature importance and combined features effect plot. - Quay Au; Julia Herbinger; Clemens Stachl; Bernd Bischl; Giuseppe Casalicchio (Data Min. Knowl. Discov., 2022)

Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar. - Wael Ghada; Éric Casellas; Julia Herbinger; Albert Garcia-Benadí; Ludwig Bothmann; Nicole Estrella; Joan Bech; Annette Menzel (Remote. Sens., 2022)

Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. - Julia Herbinger; Susanne Dandl; Fiona Katharina Ewald; Sofia Loibl; Giuseppe Casalicchio (ECAI Workshops (1), 2023)

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. - Christoph Molnar; Timo Freiesleben; Gunnar König; Julia Herbinger; Tim Reisinger; Giuseppe Casalicchio; Marvin N. Wright; Bernd Bischl (xAI (1), 2023)

Decomposing Global Feature Effects Based on Feature Interactions. - Julia Herbinger; Bernd Bischl; Giuseppe Casalicchio (CoRR, 2023)

On grouping and partitioning approaches in interpretable machine learning. - Julia Herbinger (2023)

Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. - Julian Rodemann; Federico Croppi; Philipp Arens; Yusuf Sale; Julia Herbinger; Bernd Bischl; Eyke Hüllermeier; Thomas Augustin 001; Conor J. Walsh; Giuseppe Casalicchio (CoRR, 2024)

Effector: A Python package for regional explanations. - Vasilis Gkolemis; Christos Diou; Eirini Ntoutsi; Theodore Dalamagas 001; Bernd Bischl; Julia Herbinger; Giuseppe Casalicchio (CoRR, 2024)

Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory. - Fabian Fumagalli; Maximilian Muschalik; Eyke Hüllermeier; Barbara Hammer; Julia Herbinger (AISTATS, 2025)