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.

People

Researchers

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Boaz Barak

Faculty

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Preetum Nakkiran

PhD Student

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Gal Kaplun

PhD Student

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Yamini Bansal

PhD Student

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Nikhil Vyas

PhD Student

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Sharon Qian

PhD Student

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Ben Edelman

PhD Student

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Yonadav Shavit

PhD Student

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Fred Zhang

PhD Student

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Tristan Yang

Undergraduate

Affiliated

Recent Publications

By our group and its members.

For Self-supervised Learning, Rationality Implies Generalization, Provably

The Deep Bootstrap: Good Online Learners are Good Offline Generalizers

Distributional Generalization: A New Kind of Generalization

Learning From Strategic Agents: Accuracy, Improvement, and Causality

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

Quantifying uncertainty in deep learning is a challenging and yet unsolved problem. Predictive uncertainty estimates are important to …

A major challenge in the theory of deep learning is to understand the computational complexity of learning basic families of neural …

GPT3 has shown that large generative models are unexpectedly powerful and capable. In this talk, I will review some of these …

Seminar Calendar

Join the mailing list for talk announcements.