Harvard Machine Learning Theory
We are a research group focused on building towards a theory of modern machine learning. We are interested in both experimental and theoretical approaches that advance our understanding.
Key topics include: generalization, over-parameterization, representation learning in artificial and natural networks, robustness, dynamics of SGD, and relations to kernel methods.
We also run a research-level seminar series on recent advances in the field. Join the seminar mailing list for talk announcements.
In Spring 2021, Boaz Barak taught Harvard CS 229br, a graduate level course on recent advances and open questions in the theory of machine learning and specifically deep learning.
Opportunities: We are looking for graduate students and postdocs see opportunities section below. Announcements on positions will also be posted on social media.
