• Home
• Staff & Students
• Vacancies

 

Lorenzo Rosasco

Wednesday 13th January 2016

Time: 4.00pm

 

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

 

Less is more: optimal learning with subsampling regularization

Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

In this talk, we discuss recent results on common techniques for scaling
up nonparametric methods such as kernel methods and Gaussian processes.
In particular, we focus on data dependent and independent sub-sampling
methods, namely Nystrom and random features, and study their
generalization properties within a statistical learning theory
framework. On the one hand we show that these methods can achieve
optimal learning errors while being computational efficient. On the
other hand, we show that subsampling can be seen as a form of
regularization, rather than only a way to speed up computations. [Joint
work with Raffaello Camoriano, Alessandro Rudi.]