Being Properly Improper

Tyler Sypherd, Richard Nock, Lalitha Sankar

Abstract: Properness for supervised losses stipulates that the loss function shapes the learning algorithm towards the true posterior of the data generating distribution. Unfortunately, data in modern machin...