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Loss factorization, weakly supervised learning and label noise robustness.
ICML, (2016): 708-717
We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the same loss. This holds true even for non-smooth, non-convex losses and in any RKHS. The first term is a (kernel) mean operator -- the focal quantity...More
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