| General pitfalls of model-agnostic interpretation methods for machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... International Workshop on Extending Explainable AI Beyond Deep Models and …, 2020 | 210* | 2020 |
| Relating the partial dependence plot and permutation feature importance to the data generating process C Molnar, T Freiesleben, G König, J Herbinger, T Reisinger, ... World Conference on Explainable Artificial Intelligence, 456-479, 2023 | 57 | 2023 |
| Explaining hyperparameter optimization via partial dependence plots J Moosbauer, J Herbinger, G Casalicchio, M Lindauer, B Bischl Advances in Neural Information Processing Systems 34, 2280-2291, 2021 | 55* | 2021 |
| Grouped feature importance and combined features effect plot Q Au, J Herbinger, C Stachl, B Bischl, G Casalicchio Data Mining and Knowledge Discovery 36 (4), 1401-1450, 2022 | 37 | 2022 |
| Stratiform and convective rain classification using machine learning models and micro rain radar W Ghada, E Casellas, J Herbinger, A Garcia-Benadí, L Bothmann, ... Remote Sensing 14 (18), 4563, 2022 | 12 | 2022 |
| Repid: Regional effect plots with implicit interaction detection J Herbinger, B Bischl, G Casalicchio International Conference on Artificial Intelligence and Statistics, 10209-10233, 2022 | 10 | 2022 |
| Portfolio optimization with optimal expected utility risk measures S Geissel, H Graf, J Herbinger, FT Seifried Annals of Operations Research 309 (1), 59-77, 2022 | 9* | 2022 |
| Decomposing global feature effects based on feature interactions J Herbinger, B Bischl, G Casalicchio arXiv preprint arXiv:2306.00541, 2023 | 5 | 2023 |
| Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration J Rodemann, F Croppi, P Arens, Y Sale, J Herbinger, B Bischl, ... arXiv preprint arXiv:2403.04629, 2024 | 2 | 2024 |
| Effector: A Python package for regional explanations V Gkolemis, C Diou, E Ntoutsi, T Dalamagas, B Bischl, J Herbinger, ... arXiv preprint arXiv:2404.02629, 2024 | | 2024 |
| On grouping and partitioning approaches in interpretable machine learning J Herbinger lmu, 2023 | | 2023 |
| Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation J Herbinger, S Dandl, FK Ewald, S Loibl, G Casalicchio European Conference on Artificial Intelligence, 232-249, 2023 | | 2023 |
| Relating the partial dependence plot and permutation feature importance to the data generating process T Freiesleben, C Molnar, G König, J Herbinger, T Reisinger, ... What Does Explainable AI Explain?, 2023 | | 2023 |
