Timo Freiesleben

22 papers A* 1Journal 16Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks. CoRR Timo Freiesleben
2025	J	`jnl`	Performative Validity of Recourse Explanations. CoRR Gunnar König, Hidde Fokkema, Timo Freiesleben, Celestine Mendler-Dünner, Ulrike von Luxburg
2025	J	`jnl`	The Benchmarking Epistemology: Construct Validity for Evaluating Machine Learning Models. CoRR Timo Freiesleben, Sebastian Zezulka
2024	—	`conf`	CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. xAI (3) Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright
2024	J	`jnl`	CountARFactuals - Generating plausible model-agnostic counterfactual explanations with adversarial random forests. CoRR Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright
2024	J	`jnl`	Foundation models in healthcare require rethinking reliability. Nat. Mac. Intell. Thomas Grote, Timo Freiesleben, Philipp Berens
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	J	`jnl`	Artificial Neural Nets and the Representation of Human Concepts. CoRR Timo Freiesleben
2023	—	`conf`	Dear XAI Community, We Need to Talk! - Fundamental Misconceptions in Current XAI Research. xAI (1) Timo Freiesleben, Gunnar König
2023	J	`jnl`	Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research. CoRR Timo Freiesleben, Gunnar König
2023	A*	`conf`	Improvement-Focused Causal Recourse (ICR). AAAI Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
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
2023	—	`—`	What does explainable AI explain? Timo Freiesleben
2022	J	`jnl`	Improvement-Focused Causal Recourse (ICR). CoRR Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
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
2022	J	`jnl`	The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples. Minds Mach. Timo Freiesleben
2021	J	`jnl`	A Causal Perspective on Meaningful and Robust Algorithmic Recourse. CoRR Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
2021	J	`jnl`	Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). CoRR Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup
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	J	`jnl`	Counterfactual Explanations & Adversarial Examples - Common Grounds, Essential Differences, and Potential Transfers. CoRR Timo Freiesleben
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	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