Obiettivo 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. Campo scientifico natural sciencescomputer and information sciencesartificial intelligencemachine learning Programma(i) 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) Argomento(i) ERC-SG-PE6 - ERC Starting Grant - Computer science and informatics Invito a presentare proposte ERC-2013-StG Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-SG - ERC Starting Grant Istituzione ospitante TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY Contributo UE € 1 453 802,00 Indirizzo SENATE BUILDING TECHNION CITY 32000 Haifa Israele Mostra sulla mappa Tipo di attività Higher or Secondary Education Establishments Ricercatore principale Elad Eliezer Hazan (Dr.) Contatto amministrativo Mark Davison (Prof.) Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Costo totale Nessun dato Beneficiari (1) Classifica in ordine alfabetico Classifica per Contributo UE Espandi tutto Riduci tutto TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY Israele Contributo UE € 1 453 802,00 Indirizzo SENATE BUILDING TECHNION CITY 32000 Haifa Mostra sulla mappa Tipo di attività Higher or Secondary Education Establishments Ricercatore principale Elad Eliezer Hazan (Dr.) Contatto amministrativo Mark Davison (Prof.) Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Costo totale Nessun dato