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Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention

We study the problem of monitoring machine learning models under gradual distribution shifts, where circumstances change slowly over time, often leading to unnoticed yet significant declines in accuracy. To address this, we propose Incremental …

A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models

Metrics reloaded: recommendations for image analysis validation

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