On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank

3 May 2014Ambuj TewariSougata Chaudhuri

In binary classification and regression problems, it is well understood that Lipschitz continuity and smoothness of the loss function play key roles in governing generalization error bounds for empirical risk minimization algorithms. In this paper, we show how these two properties affect generalization error bounds in the learning to rank problem... (read more)

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