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Julia Herbinger
Julia Herbinger
在 stat.uni-muenchen.de 的电子邮件经过验证
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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
572023
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
372022
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
122022
Repid: Regional effect plots with implicit interaction detection
J Herbinger, B Bischl, G Casalicchio
International Conference on Artificial Intelligence and Statistics, 10209-10233, 2022
102022
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
52023
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
22024
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
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