Objectif The statistical and computational theory of learning is one of the prime achievements of computer science and engineering. This is evident both in terms of mathematical elegance of capturing intuitive notions rigorously as well as in terms of practical applicability: machine learning has effectively reshaped the way we use information.In this proposal we tackle the very basic notions of learning. Learning theory traditional focuses on statistics and computation. We propose to add information to the characterization of learning: namely the research question we address is: how much information is necessary to learn a certain concept efficiently?The crucial difference from classical learning theory is that traditionally statistical complexity was measured in terms of the number of examples needed to learn a concept. Our question is more finely grained: what if we are allowed to inspect only parts of a given example? Can we reduce the amount of information necessary to successfully learn important concepts? This question is fundamental in understanding learning in general and designing efficient learning algorithms in particular. We show how recent advancements in convex optimization for machine learning yields positive answers to some of the above questions: there exists cases in which much more efficient algorithms exist for learning practically important concepts. Our goal is to characterize learning from the viewpoint of the amount of information necessary to learn, to design new algorithms that access less information than current state-of-the-art and are consequently significantly more efficient. New answers for these fundamental questions will be a breakthrough in our understanding of learning at large with significant potential for impact on the field of machine learning and its applications. Champ scientifique natural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) FP7-IDEAS-ERC - Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Thème(s) ERC-SG-PE6 - ERC Starting Grant - Computer science and informatics Appel à propositions ERC-2013-StG Voir d’autres projets de cet appel Régime de financement ERC-SG - ERC Starting Grant Institution d’accueil TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY Contribution de l’UE € 1 453 802,00 Adresse SENATE BUILDING TECHNION CITY 32000 Haifa Israël Voir sur la carte Type d’activité Higher or Secondary Education Establishments Chercheur principal Elad Eliezer Hazan (Dr.) Contact administratif Mark Davison (Prof.) Liens Contacter l’organisation Opens in new window Site web Opens in new window Coût total Aucune donnée Bénéficiaires (1) Trier par ordre alphabétique Trier par contribution de l’UE Tout développer Tout réduire TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY Israël Contribution de l’UE € 1 453 802,00 Adresse SENATE BUILDING TECHNION CITY 32000 Haifa Voir sur la carte Type d’activité Higher or Secondary Education Establishments Chercheur principal Elad Eliezer Hazan (Dr.) Contact administratif Mark Davison (Prof.) Liens Contacter l’organisation Opens in new window Site web Opens in new window Coût total Aucune donnée