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.




Boaz Barak



Preetum Nakkiran

PhD Student


Gal Kaplun

PhD Student


Yamini Bansal

PhD Student


Tristan Yang



Ben Edelman

PhD Student


Fred Zhang

PhD Student


Sharon Qian

PhD Student


Recent Publications

By our group and its members.

Deep Double Descent: Where Bigger Models and More Data Hurt

SGD on Neural Networks Learns Functions of Increasing Complexity

More Data Can Hurt for Linear Regression: Sample-wise Double Descent

Computational Limitations in Robust Classification and Win-Win Results

Minnorm training: an algorithm for training over-parameterized deep neural networks

Adversarial Robustness May Be at Odds With Simplicity

On the Information Bottleneck Theory of Deep Learning

Recent & Upcoming Talks

A common view of deep learning is that deep networks provide a hierarchical means of processing input data, where early layers extract …

When predictions support decisions they may influence the outcome they aim to predict. We call such predictions performative; the …

Abstract: Machine Learning is invaluable for extracting insights from large volumes of data. A key assumption enabling many methods, …

How should we go about creating a science of deep learning? One might be tempted to focus on replicability, reproducibility, and …

Seminar Calendar

Join the mailing list for talk announcements.