The incorporation of priors [30] in the Optimal Uncertainty Quantification (OUQ) framework [31] reveals brittleness in Bayesian inference; a model may share an arbitrarily large number of finite-dimensional marginals with, or be arbitrarily close (in Prokhorov or total variation metrics) to, the data-generating distribution and still make the largest possible prediction error after conditioning...

Genetic programming systems typically use a fixed training population to optimize programs according to problem-specific fitness criteria. The best-of-run programs evolved by these systems frequently exhibit optimal (or nearoptimal) performance in competitive survival environments explicitly represented by the training population. Unfortunately, subsequent performance of these programs is often...