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Timo Freiesleben
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Books and Theses
- 2023
- [b1]Timo Freiesleben:
What does explainable AI explain? Ludwig Maximilian University of Munich, Germany, 2023
Journal Articles
- 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) - 2022
- [j1]Timo Freiesleben:
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples. Minds Mach. 32(1): 77-109 (2022)
Conference and Workshop Papers
- 2023
- [c4]Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup:
Improvement-Focused Causal Recourse (ICR). AAAI 2023: 11847-11855 - [c3]Timo Freiesleben, Gunnar König:
Dear XAI Community, We Need to Talk! - Fundamental Misconceptions in Current XAI Research. xAI (1) 2023: 48-65 - [c2]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 - 2020
- [c1]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
Informal and Other Publications
- 2024
- [i10]Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright:
CountARFactuals - Generating plausible model-agnostic counterfactual explanations with adversarial random forests. CoRR abs/2404.03506 (2024) - 2023
- [i9]Timo Freiesleben, Gunnar König:
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research. CoRR abs/2306.04292 (2023) - [i8]Timo Freiesleben:
Artificial Neural Nets and the Representation of Human Concepts. CoRR abs/2312.05337 (2023) - 2022
- [i7]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) - [i6]Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup:
Improvement-Focused Causal Recourse (ICR). CoRR abs/2210.15709 (2022) - 2021
- [i5]Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup:
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). CoRR abs/2106.08086 (2021) - [i4]Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup:
A Causal Perspective on Meaningful and Robust Algorithmic Recourse. CoRR abs/2107.07853 (2021) - [i3]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
- [i2]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) - [i1]Timo Freiesleben:
Counterfactual Explanations & Adversarial Examples - Common Grounds, Essential Differences, and Potential Transfers. CoRR abs/2009.05487 (2020)
Coauthor Index
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