Niklas Koenen — accepted AI-conference papers

2026

Functional Decomposition and Shapley Interactions for Interpreting Survival Models

Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli, Niklas Koenen, Marvin N. Wright, Julia Herbinger

ICML 2026poster

Hazard and survival functions are natural, interpretable targets in time-to-event prediction, but their inherent non-additivity fundamentally limits standard additive explanation methods. We introduce Survival Functional Decomposition (SurvFD), a principled approach for analyzing feature interaction…

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2025

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

Kristin Blesch, Niklas Koenen, Jan Kapar, Pegah Golchian, Lukas Burk, Markus Loecher +1

AAAI 2025technical

This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance assesses the impact of a feature on a prediction model's performance given the information of other features. Model-agnostic…

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2025

Gradient-based Explanations for Deep Learning Survival Models

Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright

ICML 2025poster

Deep learning survival models often outperform classical methods in time-to-event predictions, particularly in personalized medicine, but their "black box" nature hinders broader adoption. We propose a framework for gradient-based explanation methods tailored to survival neural networks, extending t…

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