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arXiv · 1412.1619

Fast Rates by Transferring from Auxiliary Hypotheses

Ilja Kuzborskij·Francesco Orabona
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Abstract

In this work we consider the learning setting where, in addition to the training set, the learner receives a collection of auxiliary hypotheses originating from other tasks. We focus on a broad class of ERM-based linear algorithms that can be instantiated with any non-negative smooth loss function and any strongly convex regularizer. We establish generalization and excess risk bounds, showing that, if the algorithm is fed with a good combination of source hypotheses, generalization happens at the fast rate $\mathcal{O}(1/m)$ instead of the usual $\mathcal{O}(1/\sqrt{m})$. On the other hand, if the source hypotheses combination is a misfit for the target task, we recover the usual learning rate. As a byproduct of our study, we also prove a new bound on the Rademacher complexity of the smooth loss class under weaker assumptions compared to previous works.

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BibTeXRIS

Ilja Kuzborskij, Francesco Orabona. 2015-07-17. Fast Rates by Transferring from Auxiliary Hypotheses. https://doi.org/10.1007/s10994-016-5594-4

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