| 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 …, 2022 | 147 | 2022 |
| Model-agnostic Feature Importance and Effects with Dependent Features--A Conditional Subgroup Approach C Molnar, G König, B Bischl, G Casalicchio Data Mining and Knowledge Discovery, 2023 | 77 | 2023 |
| Relative Feature Importance G König, C Molnar, B Bischl, M Grosse-Wentrup 2020 25th International Conference on Pattern Recognition (ICPR), 9318--9325, 2021 | 56 | 2021 |
| Relating the partial dependence plot and permutation feature importance to the data generating process G König*, C Molnar*, T Freiesleben*, J Herbinger, T Reisinger, ... World Conference on Explainable Artificial Intelligence, 456-479, 2023 | 55 | 2023 |
| Pitfalls to avoid when interpreting machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... ICML Workshop on XXAI: Extending Explainable AI Beyond Deep Models and …, 2020 | 55 | 2020 |
| A Causal Perspective on Meaningful and Robust Algorithmic Recourse G König, T Freiesleben, M Grosse-Wentrup ICML 2021 Workshop on Algorithmic Recourse; arXiv:2107.07853, 2021 | 19 | 2021 |
| Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena T Freiesleben, G König, C Molnar, A Tejero-Cantero arXiv preprint arXiv:2206.05487, 2022 | 16 | 2022 |
| Improvement-Focused Causal Recourse (ICR) G König, T Freiesleben, M Grosse-Wentrup The 37th AAAI Conference on Artificial Intelligence, 2022 | 14 | 2022 |
| Dear XAI community, we need to talk! Fundamental misconceptions in current XAI research T Freiesleben, G König World Conference on Explainable Artificial Intelligence, 48-65, 2023 | 12 | 2023 |
| Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT) G König, T Freiesleben, B Bischl, G Casalicchio, M Grosse-Wentrup arXiv preprint arXiv:2106.08086, 2021 | 5 | 2021 |
| Efficient SAGE Estimation via Causal Structure Learning G König*, C Luther*, M Grosse-Wentrup AISTATS 2023, 2023 | 3* | 2023 |
| Deep Learning in Objective Classification of Spontaneous Movement of Patients with Parkinson’s Disease Using Large-Scale Free-Living Sensor Data F Pfister, D Kulić, T Um, D Pichler, A Ahmadi, M Lang, G König, F Achilles, ... | 1 | 2017 |
| A Guide to Feature Importance Methods for Scientific Inference FK Ewald, L Bothmann, MN Wright, B Bischl, G Casalicchio, G König arXiv preprint arXiv:2404.12862, 2024 | | 2024 |
| CountARFactuals--Generating plausible model-agnostic counterfactual explanations with adversarial random forests G König*, S Dandl*, K Blesch*, T Freiesleben*, J Kapar, B Bischl, ... arXiv preprint arXiv:2404.03506, 2024 | | 2024 |
| If interpretability is the answer, what is the question? G König lmu, 2023 | | 2023 |
| A Causal Perspective on Challenges for AI in Precision Medicine G König, M Grosse-Wentrup Proceedings of the 2nd International Congress on Precision Medicine (PMBC), 2019 | | 2019 |
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichEmail được xác minh tại stat.uni-muenchen.de
