| General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... xxAI - Beyond Explainable AI. xxAI 2020. Lecture Notes in Computer Science 13200, 2022 | 253* | 2022 |
| Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations CA Scholbeck, C Molnar, C Heumann, B Bischl, G Casalicchio Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019 …, 2020 | 45 | 2020 |
| Marginal Effects for Non-Linear Prediction Functions CA Scholbeck, G Casalicchio, C Molnar, B Bischl, C Heumann Data Mining and Knowledge Discovery 38, 2997–3042, 2024 | 13 | 2024 |
| Algorithm-Agnostic Feature Attributions for Clustering CA Scholbeck, H Funk, G Casalicchio Explainable Artificial Intelligence. xAI 2023. Communications in Computer …, 2023 | 5* | 2023 |
| Bridging gaps in interpretable machine learning CA Scholbeck lmu, 2024 | | 2024 |
| Bridging the Gap Between Machine Learning and Sensitivity Analysis CA Scholbeck, J Moosbauer, G Casalicchio, H Gupta, B Bischl, ... arXiv preprint arXiv:2312.13234, 2023 | | 2023 |
| fmeffects: An R Package for Forward Marginal Effects H Löwe, CA Scholbeck, C Heumann, B Bischl, G Casalicchio arXiv preprint arXiv:2310.02008, 2023 | | 2023 |
