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Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition. (arXiv:2210.12256v1 [cs.LG])
Oct. 25, 2022, 1:11 a.m. | Sebastian Gruber, Florian Buettner
cs.LG updates on arXiv.org arxiv.org
Reliably estimating the uncertainty of a prediction throughout the model
lifecycle is crucial in many safety-critical applications.
The most common way to measure this uncertainty is via the predicted
confidence. While this tends to work well for in-domain samples, these
estimates are unreliable under domain drift.
Alternatively, a bias-variance decomposition allows to directly measure the
predictive uncertainty across the entire input space.
But, such a decomposition for proper scores does not exist in current
literature, and for exponential families it …
arxiv bias bias-variance general predictions uncertainty variance
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