Predictive Uncertainty as a Robustness Indicator: Quantifying Model Reliability Under Real World Noise and Distribution Shifts

This paper is aimed at providing a detailed survey and integration of up-to-date research on Predictive Uncertainty Quantification in Machine Learning Systems, specifically addressing the problem of model robustness in the light of distributional shifts and noise under realistic conditions. We describe some methodologies in Bayesian deep learning, conformal prediction, and uncertainty-aware decision making through their theoretical descriptions as well as practical applications to assess model reliability under non-static situations. In contrast to suggesting new algorithms or an experimental study, this work rather presents a preliminary explanatory frame which helps researchers and developers appreciate how predictive uncertainties might facilitate dependable deployment for safety-critical and high-stake uses.