What Uncertainties Do We Need for Dynamical Systems?
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Computer Science > Machine Learning
Title:What Uncertainties Do We Need for Dynamical Systems?
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.
| Comments: | EIML@ICML |
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2606.11988 [cs.LG] |
| (or arXiv:2606.11988v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.11988
arXiv-issued DOI via DataCite (pending registration)
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