About
News
Projects
Publications
Team
Teaching
Jobs
Contact
Light
Dark
Automatic
highlight
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
Cite
×