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Adaptation in Online Learning through Dimension-Free Exponentiated Gradient

Speaker: 
Prof. Francesco Orabona, TTI Chicago, USA
Data dell'evento: 
Thursday, 19 December, 2013 - 14:30
Luogo: 
Aula Magna
Contatto: 
caputo@dis.uniroma1.it

As the big data paradigm is gaining momentum, learning algorithms trained through fast stochastic gradient descent methods are becoming the de-facto standard in the industry world. Still, even these simple procedures cannot be used completely "off-the-shelf" because parameters, e.g. the learning rate, has to be properly tuned to the particular problem to achieve fast convergence.

The online learning framework is a powerful tool to design fast learning algorithms able to work in both the stochastic and adversarial setting.
In this talk I will introduce new advancements in the time-varying regularization framework for online learning, that allows to derive almost parameter-free adaptive algorithms. In particular, I will focus on a new algorithm based on a dimension-free exponentiated gradient. Contrary to the existing online algorithms, it achieves an optimal regret bound, up to logarithmic terms, without any parameter nor any prior knowledge about the optimal solution.

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