Transfer bounds for linear feature learning

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Abstract

If regression tasks are sampled from a distribution, then the expected error for a future task can be estimated by the average empirical errors on the data of a finite sample of tasks, uniformly over a class of regularizing or pre-processing transformations. The bound is dimension free, justifies optimization of the pre-processing feature-map and explains the circumstances under which learning-to-learn is preferable to single task learning.

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