no code implementations • 21 Mar 2016 • Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran-Dinh, Luca Baldassarre, Volkan Cevher
Our experimental findings on synthetic and real applications support our claims for faster recovery in the convex setting -- as opposed to using dense sensing matrices, while showing a competitive recovery performance.
no code implementations • 21 Oct 2015 • Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran Gözcü, Ilija Bogunovic, Volkan Cevher
In this paper, we instead take a principled learning-based approach in which a \emph{fixed} index set is chosen based on a set of training signals $\mathbf{x}_1,\dotsc,\mathbf{x}_m$.
no code implementations • 20 Jul 2015 • Anastasios Kyrillidis, Luca Baldassarre, Marwa El-Halabi, Quoc Tran-Dinh, Volkan Cevher
For each, we present the models in their discrete nature, discuss how to solve the ensuing discrete problems and then describe convex relaxations.
no code implementations • 25 Mar 2013 • Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil
During the past years there has been an explosion of interest in learning methods based on sparsity regularization.
no code implementations • 13 Mar 2013 • Luca Baldassarre, Nirav Bhan, Volkan Cevher, Anastasios Kyrillidis, Siddhartha Satpathi
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing.
no code implementations • 18 Jun 2012 • Steffen Grunewalder, Guy Lever, Luca Baldassarre, Massi Pontil, Arthur Gretton
For policy optimisation we compare with least-squares policy iteration where a Gaussian process is used for value function estimation.