Section: Partnerships and Cooperations

European Initiatives

FP7 Projects


Participants : Francis Bach [correspondant] , Simon Lacoste-Julien, Augustin Lefèvre, Nicolas Le Roux, Mark Schmidt.

  • Title: SIERRA – Sparse structured methods for machine learning

  • Type: IDEAS

  • Instrument: ERC Starting Grant (Starting)

  • Duration: December 2009 - November 2014

  • Coordinator: Inria (France)

  • See also: http://www.di.ens.fr/~fbach/sierra

  • Abstract: Machine learning is now a core part of many research domains, where the abundance of data has forced researchers to rely on automated processing of information. The main current paradigm of application of machine learning techniques consists in two sequential stages: in the representation phase, practitioners first build a large set of features and potential responses for model building or prediction. Then, in the learning phase, off-the-shelf algorithms are used to solve the appropriate data processing tasks. While this has led to significant advances in many domains, the potential of machine learning techniques is far from being reached: the tenet of this proposal is that to achieve the expected breakthroughs, this two-stage paradigm should be replaced by an integrated process where the