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Timo Freiesleben
Timo Freiesleben
Postdoc, University of Tübingen, Cluster "Machine Learning for Science"
在 uni-tuebingen.de 的电子邮件经过验证 - 首页
标题
引用次数
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General pitfalls of model-agnostic interpretation methods for machine learning models
C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ...
Lecture Notes in Computer Science, 2022
223*2022
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
T Freiesleben
Minds and Machines, 2021
79*2021
Relating the partial dependence plot and permutation feature importance to the data generating process
T Freiesleben*, C Molnar*, G König*, J Herbinger, T Reisinger, ...
World Conference on Explainable Artificial Intelligence, 456-479, 2023
622023
Beyond generalization: a theory of robustness in machine learning
T Freiesleben, T Grote
Synthese, 2023
222023
A causal perspective on meaningful and robust algorithmic recourse
G König, T Freiesleben, M Grosse-Wentrup
ICML Workshop, 2021
222021
Improvement-focused causal recourse (ICR)
G König, T Freiesleben, M Grosse-Wentrup
AAAI Conference on Artificial Intelligence, 2023
182023
Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
T Freiesleben, G König, C Molnar, Á Tejero-Cantero
Minds and Machines, 2024
172024
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research
T Freiesleben, G König
World XAI Conference, 2023
152023
Artificial Neural Nets and the Representation of Human Concepts
T Freiesleben
arXiv preprint arXiv:2312.05337, 2023
12023
CountARFactuals--Generating plausible model-agnostic counterfactual explanations with adversarial random forests
T Freiesleben*, S Dandl*, K Blesch*, G König*, J Kapar, B Bischl, ...
World XAI Conference, 2024
2024
Supervised Machine Learning for Science: How to stop worrying and love your black box
T Freiesleben*, C Molnar*
https://ml-science-book.com/, 2024
2024
What Does Explainable AI Explain?
T Freiesleben
Disertation LMU Munich, 2023
2023
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