The Geometry of Losses


Robert C. Williamson ;
Proceedings of The 27th Conference on Learning Theory, PMLR 35:1078-1108, 2014.


Loss functions are central to machine learning because they are the means by which the quality of a prediction is evaluated. Any loss that is not proper, or can not be transformed to be proper via a link function is inadmissible. All admissible losses for n-class problems can be obtained in terms of a convex body in \mathbbR^n. We show this explicitly and show how some existing results simplify when viewed from this perspective. This allows the development of a rich algebra of losses induced by binary operations on convex bodies (that return a convex body). Furthermore it allows us to define an “inverse loss” which provides a universal “substitution function” for the Aggregating Algorithm. In doing so we show a formal connection between proper losses and norms.

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