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Maximilian Muschalik

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#machine learning Preprint Oct 2026

Credal Machine Learning for Risk-Averse Decision Making

In many machine learning applications, it is necessary to guard against worst-case scenarios and predictions that could result in substantial losses. In principle, this can be achieved by training risk-averse predictive models that minimize loss functions such as conditional value-at-risk (CVaR), rather than relying on...

Tim Löhr, Paul Hofman, Maximilian Muschalik et al. · 0 citations

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