Section: Partnerships and Cooperations

European Initiatives

FP7 Projects

SIERRA

• Title: SIERRA – Sparse structured methods for machine learning

• Type: IDEAS

• Instrument: ERC Starting Grant

• 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 specific structure of a problem is taken into account explicitly in the learning process. Considering such structure appropriately allows the consideration of massive numbers of features or potentially the on-demand construction of relevant features, in both numerically efficient and theoretically understood ways. Thus, one could get the benefits of very large numbers of features—e.g., better predictive performance—in a reasonable running time.

  This problem will be attacked through the tools of regularization by sparsity-inducing norms, that have recently led to theoretical and algorithmic advances, as well as practical successes, in unstructured domains. The scientific objective is thus to marry structure with sparsity: this is particularly challenging because structure may occur in various ways (discrete, continuous or mixed) and the targeted applications in computer vision and audio processing lead to large-scale convex optimization problems.