Christoph Molnar

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 22

Top Topics

Machine Learning

Feature Importance

Functional Decomposition

Top Venues

PKDD/ECML Workshops (1)

Publications

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) (2024)
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)
Timo Freiesleben, Gunnar König, Christoph Molnar, Álvaro Tejero-Cantero
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena. CoRR (2022)
Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann
Marginal Effects for Non-Linear Prediction Functions. CoRR (2022)
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 (2021)
Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup
Relative Feature Importance. ICPR (2020)
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)
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. CoRR (2020)
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops (2020)
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 (2020)
Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl
Multi-Objective Counterfactual Explanations. PPSN (1) (2020)
Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio
Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach. CoRR (2020)
Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup
Relative Feature Importance. CoRR (2020)
Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl
Multi-Objective Counterfactual Explanations. CoRR (2020)
Andreas Holzinger, Anna Saranti, Christoph Molnar, Przemyslaw Biecek, Wojciech Samek
Explainable AI Methods - A Brief Overview. xxAI@ICML (2020)
Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations. CoRR (2019)
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. CoRR (2019)
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. PKDD/ECML Workshops (1) (2019)
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)
Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
Visualizing the Feature Importance for Black Box Models. ECML/PKDD (1) (2018)
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
iml: An R package for Interpretable Machine Learning. J. Open Source Softw. 3 (26) (2018)
Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
Visualizing the Feature Importance for Black Box Models. CoRR (2018)