A Ground-Truth Framework for Uncertainty Disentanglement with Posterior Risk
Reliable uncertainty estimates are critical in safety-sensitive applications. For such estimates to be useful in practice, it is crucial to understand the sources underlying a model's uncertainty, motivating the disentanglement of total uncertainty into epistemic and aleatoric uncertainty. Existing notions of uncertainty differ in the sources they capture and, consequently, in their definitions of aleatoric and epistemic uncertainty, with no universally accepted definition. We define uncertainty through sample-conditional pointwise posterior risk, which is the expected loss of a predictor under the distribution of plausible ground-truth functions given the observed sample. This definition...
| Authors | Frieder Wizgall, Georg Tirpitz, Moritz Seiler et al. |
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| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 1 October 2026 |
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