18

Mar

Teaser image to Leveraging predictive uncertainty to enable reliable deployment of AI models

Lecture

Leveraging Predictive Uncertainty to Enable Reliable Deployment of AI Models

Florian Buettner, Goethe-University Frankfurt

   18.03.2025

   10:30 am - 11:30 am

   LMU Munich, Ludwigstr. 28 VG/II; Room 211b

This talk explores a unified theoretical framework for uncertainty quantification in machine learning, extending traditional methods to modern applications like generative modeling. By generalizing the bias-variance decomposition using proper scores, it introduces Bregman Information as a key component for epistemic uncertainty estimation. The session also covers uncertainty calibration, highlighting relationships between different calibration metrics. Finally, a novel approach for uncertainty-aware performance monitoring is presented, improving reliability under distribution shifts through active labeling interventions.

Organized by:

LMU Munich School of Management


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