Beyond L2-loss functions for learning sparse models

In sparse learning, the squared Euclidean distance is a popular choice for measuring the approximation quality. However, the use of other forms of parametrized loss functions, including asymmetric losses, has generated research interest. In this paper, we perform sparse learning using a broad class of smooth piecewise linear quadratic (PLQ) loss functions, including robust and asymmetric losses that are adaptable to many real-world scenarios....