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Surrogate Regret Bounds for Proper Losses
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. We present tight surrogate regret bounds for the class of proper (i.e., Fisher consistent) losses. The bounds generalise the margin-based bounds due to Bartlett et al. (2006). The proof uses Taylor's theorem and leads to new representations for loss and regret and a simple proof of the integral representation of proper losses. We also present a different formulation of a duality result of Bregman divergences which leads to a demonstration of the convexity of composite losses using canonical link functions.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
