6 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 1 cited
Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks
Timo Freiesleben
Recent work in machine learning increasingly attributes human-like capabilities such as reasoning or theory of mind to large language models (LLMs) on the basis of benchmark perfor…
cs.LG2025
The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models
Timo Freiesleben, Sebastian Zezulka
Predictive benchmarking, evaluating machine learning models based on predictive performance and competitive ranking, is central to machine learning research and scientific inquiry.…
cs.AI2020★ 6 cited
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
Timo Freiesleben
The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This…