What Uncertainties Do We Need for Dynamical Systems?

MCML Authors

Yusuf Sale

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Christopher Bülte

→ Group Gitta Kutyniok
 Mathematical Foundations of Artificial Intelligence

Joshua Stiller

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Core PI

Artificial Intelligence and Machine Learning

Abstract

The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling. In this paper, we offer a machine learning perspective on uncertainty modeling for dynamical systems, which has been studied much less so far. In particular, we ask: what uncertainties do we need for dynamical systems? We discuss sources of uncertainty, clarify their nature (aleatoric or epistemic), and consider how the objectives of representing and quantifying uncertainty vary across different tasks.

inproceedings SBC+26

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EIML @ICML 2026

Workshop on Epistemic Intelligence in Machine Learning at the 43rd International Conference on Machine Learning. Seoul, South Korea, Jul 06-11, 2026. To be published. Preprint available.

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Authors

Y. Sale • C. Bülte • F. Czaja • J. Stiller • E. Hüllermeier

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Links

arXiv

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Research Areas

 A2 | Mathematical Foundations

 A3 | Computational Models