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ORCIDChristoph Molnar
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2020 – today
- 2024
[j4]Christoph Molnar
, Gunnar König, Bernd Bischl, Giuseppe Casalicchio:
Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Min. Knowl. Discov. 38(5): 2903-2941 (2024)- [j3]Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann:
Marginal effects for non-linear prediction functions. Data Min. Knowl. Discov. 38(5): 2997-3042 (2024) - [j2]Timo Freiesleben, Gunnar König, Christoph Molnar, Álvaro Tejero-Cantero:
Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena. Minds Mach. 34(3): 32 (2024) - 2023
- [c9]Christoph Molnar, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl:
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. xAI (1) 2023: 456-479 - 2022
- [b1]Christoph Molnar:
Model-agnostic interpretable machine learning. Ludwig Maximilian University of Munich, Germany, 2022 - [i11]Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann:
Marginal Effects for Non-Linear Prediction Functions. CoRR abs/2201.08837 (2022) - [i10]Timo Freiesleben, Gunnar König, Christoph Molnar, Álvaro Tejero-Cantero:
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena. CoRR abs/2206.05487 (2022) - 2021
- [i9]Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl:
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. CoRR abs/2109.01433 (2021) - 2020
- [c8]Andreas Holzinger, Anna Saranti, Christoph Molnar, Przemyslaw Biecek, Wojciech Samek:
Explainable AI Methods - A Brief Overview. xxAI@ICML 2020: 13-38 - [c7]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. xxAI@ICML 2020: 39-68 - [c6]Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup:
Relative Feature Importance. ICPR 2020: 9318-9325 - [c5]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops 2020: 417-431 - [c4]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. PPSN (1) 2020: 448-469 - [i8]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. CoRR abs/2004.11165 (2020) - [i7]Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio:
Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach. CoRR abs/2006.04628 (2020) - [i6]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
Pitfalls to Avoid when Interpreting Machine Learning Models. CoRR abs/2007.04131 (2020) - [i5]Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup:
Relative Feature Importance. CoRR abs/2007.08283 (2020) - [i4]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. CoRR abs/2010.09337 (2020)
2010 – 2019
- 2019
- [c3]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. PKDD/ECML Workshops (1) 2019: 193-204 - [c2]Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio:
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. PKDD/ECML Workshops (1) 2019: 205-216 - [i3]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. CoRR abs/1904.03867 (2019) - [i2]Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio:
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations. CoRR abs/1904.03959 (2019) - 2018
- [j1]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
iml: An R package for Interpretable Machine Learning. J. Open Source Softw. 3(26): 786 (2018) - [c1]Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl:
Visualizing the Feature Importance for Black Box Models. ECML/PKDD (1) 2018: 655-670 - [i1]Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl:
Visualizing the Feature Importance for Black Box Models. CoRR abs/1804.06620 (2018)
Coauthor Index
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