11 citations · 12 across the 2 of their papers we have counts for
3 papers · 1 filter
Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition
Sebastian G. Gruber, Florian Buettner
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…
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
Sebastian G. Gruber, Florian Buettner
With model trustworthiness being crucial for sensitive real-world applications, practitioners are putting more and more focus on improving the uncertainty calibration of deep neura…
Post-hoc Uncertainty Calibration for Domain Drift Scenarios
Christian Tomani, Sebastian Gruber, Muhammed Ebrar Erdem +2
We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representativ…