Gunnar König

According to our database, Gunnar König authored at least 16 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena.

Timo Freiesleben, Gunnar König, Christoph Molnar, Álvaro Tejero-Cantero

Minds Mach., September, 2024

Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach.

Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio

Data Min. Knowl. Discov., September, 2024

Disentangling Interactions and Dependencies in Feature Attribution.

Gunnar König, Eric Günther, Ulrike von Luxburg

CoRR, 2024

A Guide to Feature Importance Methods for Scientific Inference.

Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König

Proceedings of the Explainable Artificial Intelligence, 2024

CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests.

Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright

Proceedings of the Explainable Artificial Intelligence, 2024

2023

If interpretability is the answer, what is the question?: a causal perspective.

Gunnar König

PhD thesis, 2023

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.

Christoph Molnar, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl

Proceedings of the Explainable Artificial Intelligence, 2023

Dear XAI Community, We Need to Talk! - Fundamental Misconceptions in Current XAI Research.

Timo Freiesleben, Gunnar König

Proceedings of the Explainable Artificial Intelligence, 2023

Efficient SAGE Estimation via Causal Structure Learning.

Christoph Luther, Gunnar König, Moritz Grosse-Wentrup

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Improvement-Focused Causal Recourse (ICR).

Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2021

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.

Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl

CoRR, 2021

A Causal Perspective on Meaningful and Robust Algorithmic Recourse.

Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup

CoRR, 2021

Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT).

Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup

CoRR, 2021

2020

Pitfalls to Avoid when Interpreting Machine Learning Models.

Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl

CoRR, 2020

Relative Feature Importance.

Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup

Proceedings of the 25th International Conference on Pattern Recognition, 2020

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.

Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl

Proceedings of the xxAI - Beyond Explainable AI, 2020