106 citations · 525 across the 44 of their papers we have counts for
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Fast Rates by Transferring from Auxiliary Hypotheses
Ilja Kuzborskij, Francesco Orabona
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 foc…
Scalable Greedy Algorithms for Transfer Learning
Ilja Kuzborskij, Francesco Orabona, Barbara Caputo
In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constr…
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
Francesco Orabona
Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability. While various methods have been…
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations
H. Brendan McMahan, Francesco Orabona
We study algorithms for online linear optimization in Hilbert spaces, focusing on the case where the player is unconstrained. We develop a novel characterization of a large class o…