Learning without Concentration for General Loss Functions

13 Oct 2014 Shahar Mendelson

We study prediction and estimation problems using empirical risk minimization, relative to a general convex loss function. We obtain sharp error rates even when concentration is false or is very restricted, for example, in heavy-tailed scenarios... (read more)

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