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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