We examine gradient descent on unregularized logistic regression problems, with homogeneous linear predictors on linearly separable …
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, 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.