To improve the efficiency and sustainability of learning deep models, we
propose CREST, the first scalable framework with rigorous theoretical
guarantees to identify the most valuable examples for training non-convex
models, particularly deep networks. To guarantee convergence to a stationary
point of a non-convex function, CREST models the non-convex loss a