📋 Tender Overview
Foundations Of Generalization
Objective
Arguably, the most crucial objective of Learning Theory is to understand the basic notion of generalization: How can a learning agent infer from a finite amount of data to the whole population? Today's learning algorithms are poorly understood from that perspective. In particular, best practices, such as using highly overparameterized models to fit relatively few data, seem to be in almost contradiction to common wisdom, and classical models of learning seem to be incapable of explaining the impressive success of such algorithms. The objective of this proposal is to understand generalization in overparameterized models and understand the role of algorithms in learning. Toward this task, I will consider two mathematical models of learning that shed light on this fundamental problem.
Funded Under
European Research Council (Erc)
Total Cost
€ 1 419 375,00
Eu Contribution
€ 1 419 375,00
Coordinated By
Tel Aviv University
Israel
