Research Activities

Main research topics:

I have been working on different topics including low-rank approximations, kernel learning, multi-class classification, generalization properties, convex optimization, algorithmic properties.

In a nutshell, I am trying to work on algorithms and methods that are theoretically founded but aim at achieving properties that are of practical interest for Machine Learning (e.g. computational efficiency, good generalization properties).

Please see the publications for further information.

Beforehand, I have also been working on multi-view and semi-supervised learning. Mainly, I have focused on co-training.