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A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance
Provably Better Explanations with Optimized Aggregation of Feature Attributions
Through the Eyes of the Expert: Aligning Human and Machine Attention for Industrial AI
Encoding domain knowledge in multi-view latent variable models: A bayesian approach with structured sparsity
Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions
Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition
Multi-output Gaussian Processes for uncertainty-aware recommender systems
Towards trustworthy predictions from deep neural networks with fast adversarial calibration
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