Speaker:

Ohad Shamir

Talk Title:

Towards a Theory of Deep Learning

Date and Location:

Thursday, March 27, 2025

Bahen Centre for Information Technology, BA 3200

This lecture is open to the public. No registration is required, but space is limited.

Abstract:

The rise of deep learning, in the form of artificial neural networks, has undoubtedly been the most important development in machine learning over the past decade. However, our scientific understanding of deep learning remains very limited, with much of the field's progress being driven by innovative heuristics, along with a dramatic scaling of data and computation (which may soon reach its limits).  In this talk, I'll describe several research directions spearheaded by my group, aimed at developing a rigorous foundation for the field. These involve topics such as how the network architecture shapes the types of input-output mappings that it can compute, the ability of large-scale predictors to fit noisy training data while achieving good test performance, as well as the implicit biases of standard training algorithms and their implications for robustness and privacy.

About Ohad Shamir:

Ohad Shamir is a professor in the computer science department at the Weizmann Institute of Science, with previous research roles at Microsoft and Google. His work focuses on theoretical machine learning, in areas such as theory of deep learning, the intersection of machine learning and optimization, and learning under information and communication constraints. He served as a program co-chair of the Conference on Learning Theory (COLT), as well as a member of its steering committee, and as an action editor of the Journal of Machine Learning Research (JMLR). His honors include the inaugural 2024 Prize in the Mathematics of Artificial Intelligence, several best paper awards, the Hebrew University's PhD thesis prize, and a €1.5 million ERC grant.