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Julia Herbinger
Julia Herbinger
Verified email at stat.uni-muenchen.de
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Cited by
Year
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
259*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
792023
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
74*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
492022
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
142022
Repid: Regional effect plots with implicit interaction detection
J Herbinger, B Bischl, G Casalicchio
International Conference on Artificial Intelligence and Statistics, 10209-10233, 2022
132022
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
11*2022
Decomposing global feature effects based on feature interactions
J Herbinger, B Bischl, G Casalicchio
arXiv preprint arXiv:2306.00541, 2023
92023
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
52024
General pitfalls of model-agnostic interpretation methods for machine learning models, 2020
C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ...
URL: https://arxiv. org/abs/2007 4131 (4), 0
3
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
22023
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
12024
On grouping and partitioning approaches in interpretable machine learning
J Herbinger
lmu, 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
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