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 [BibTeX] [Marc21]
Fast Rates by Transferring from Auxiliary Hypotheses
Type of publication: Journal paper
Citation: Kuzborskij_MLJ_2016
Publication status: Accepted
Journal: Machine Learning
Year: 2016
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 O(1/m) instead of the usual 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.
Projects Idiap
Authors Kuzborskij, Ilja
Orabona, Francesco
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