Encoding Causal Macrovariables. - Benedikt Höltgen (CoRR, 2021)

DeDUCE: Generating Counterfactual Explanations Efficiently. - Benedikt Höltgen; Lisa Schut; Jan Markus Brauner; Yarin Gal (CoRR, 2021)

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. - Sören Mindermann; Jan Markus Brauner; Muhammed Razzak; Mrinank Sharma; Andreas Kirsch 002; Winnie Xu; Benedikt Höltgen; Aidan N. Gomez; Adrien Morisot; Sebastian Farquhar; Yarin Gal (ICML, 2022)

On the Richness of Calibration. - Benedikt Höltgen; Robert C. Williamson (FAccT, 2023)

Causal modelling without counterfactuals and individualised effects. - Benedikt Höltgen; Robert C. Williamson (CoRR, 2024)

Five reasons against assuming a data-generating distribution in Machine Learning. - Benedikt Höltgen; Robert C. Williamson (CoRR, 2024)

Reconsidering Fairness Through Unawareness from the Perspective of Model Multiplicity. - Benedikt Höltgen; Nuria Oliver (CoRR, 2025)