  Safe deployment of AI models requires proactive detection of failures to
prevent costly errors. To this end, we study the important problem of detecting
failures in deep regression models. Existing approaches rely on epistemic
uncertainty estimates or inconsistency w.r.t the training data to identify
failure. Interestingly, we find that while uncertainties are necessary they are
insufficient to accurately characterize failure in practice. Hence, we
introduce PAGER (Principled Analysis of Generalization Errors in Regressors), a
framework to systematically detect and characterize failures in deep
regressors. Built upon the principle of anchored training in deep models, PAGER
unifies both epistemic uncertainty and complementary manifold non-conformity
scores to accurately organize samples into different risk regimes.
