Christoph Molnar

26 papers B 1Journal 16Unranked 8

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Correction: Marginal effects for non-linear prediction functions. Data Min. Knowl. Discov. Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
2024	J	`jnl`	Marginal effects for non-linear prediction functions. Data Min. Knowl. Discov. Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
2024	J	`jnl`	Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Min. Knowl. Discov. Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio
2024	J	`jnl`	Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena. Minds Mach. Timo Freiesleben, Gunnar König, Christoph Molnar, Álvaro Tejero-Cantero
2023	—	`conf`	Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. xAI (1) Christoph Molnar, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl
2022	J	`jnl`	Marginal Effects for Non-Linear Prediction Functions. CoRR Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
2022	—	`—`	Model-agnostic interpretable machine learning. Christoph Molnar
2022	J	`jnl`	Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena. CoRR Timo Freiesleben, Gunnar König, Christoph Molnar, Álvaro Tejero-Cantero
2021	J	`jnl`	Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. CoRR Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl
2020	—	`conf`	Explainable AI Methods - A Brief Overview. xxAI@ICML Andreas Holzinger, Anna Saranti, Christoph Molnar, Przemyslaw Biecek, Wojciech Samek
2020	—	`conf`	General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. xxAI@ICML Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl
2020	—	`conf`	Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2020	J	`jnl`	Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. CoRR Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2020	J	`jnl`	Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach. CoRR Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio
2020	—	`conf`	Multi-Objective Counterfactual Explanations. PPSN (1) Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl
2020	J	`jnl`	Multi-Objective Counterfactual Explanations. CoRR Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl
2020	J	`jnl`	Pitfalls to Avoid when Interpreting Machine Learning Models. CoRR Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl
2020	B	`conf`	Relative Feature Importance. ICPR Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup
2020	J	`jnl`	Relative Feature Importance. CoRR Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup
2019	J	`jnl`	Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. CoRR Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2019	—	`conf`	Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. PKDD/ECML Workshops (1) Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
2019	J	`jnl`	Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations. CoRR Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
2019	—	`conf`	Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. PKDD/ECML Workshops (1) Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
2018	—	`conf`	Visualizing the Feature Importance for Black Box Models. ECML/PKDD (1) Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
2018	J	`jnl`	Visualizing the Feature Importance for Black Box Models. CoRR Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
2018	J	`jnl`	iml: An R package for Interpretable Machine Learning. J. Open Source Softw. Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl