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Found 6 papers, 4 papers with code
Optimizing Estimators of Squared Calibration Errors in Classification
In this work, we propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors in practical settings.
Disentangling Mean Embeddings for Better Diagnostics of Image Generators
The evaluation of image generators remains a challenge due to the limitations of traditional metrics in providing nuanced insights into specific image regions.
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Proper scoring rules evaluate the quality of probabilistic predictions, playing an essential role in the pursuit of accurate and well-calibrated models.
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
For example, natural language approaches cannot be transferred to image generation.
Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition
In this work we introduce a general bias-variance decomposition for proper scores, giving rise to the Bregman Information as the variance term.
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
With model trustworthiness being crucial for sensitive real-world applications, practitioners are putting more and more focus on improving the uncertainty calibration of deep neural networks.
