Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses

The design of convex, calibrated surrogate losses, whose minimization entails consistency with respect to a desired target loss, is an important concept to have emerged in the theory of machine learni