(This list is not comprehensive. Also we’re sometimes slow in updates, see individual homepages and the arXiv for the latest publications.)

Contrasting random and learned features in deep Bayesian linear regression

Deconstructing Distributions: A Pointwise Framework of Learning

Depth induces scale-averaging in overparameterized linear Bayesian neural networks

Neural Networks as Kernel Learners: The Silent Alignment Effect

Inductive Biases and Variable Creation in Self-Attention Mechanisms

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?

Revisiting Model Stitching to Compare Neural Representations

Learning Curves for SGD on Structured Features

Out-of-Distribution Generalization in Kernel Regression

Asymptotics of Representation Learning in Finite Bayesian Neural Networks

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

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We are looking for graduate students and postdocs in the theory of machine learning.

For graduate students we have openings in Computer Science, Electrical Engineering,applied mathematics or statistics degrees. If you are applying for graduate studies in CS interested in the theory of machine learning, please mark both “Machine Learning” and “Theory of Computation” as areas of interest. Please also list the names of faculty you want to work with on your application. ML theory group faculty include Demba Ba (Electrical Engineering and Bioengineering), Boaz Barak and Sham Kakade (Computer Science), Cengiz Pehlevan (Applied Mathematics), and Lucas Janson (Statistics). There are also ML theory affiliated faculty in all of the above departments and more. All of us are also open to the possibilities of co-advising students, including across different departments and schools.

Other opportunities include:

• Postdoctoral Fellow in the Theory of Representation Learning in Artificial and Natural Systems

• Postdoc position on machine learning and applications at MIT with Sasha Rakhlin, jointly supervised by Boaz Barak. (Second year may be at Harvard.)

• The George F. Carrier Postdoctoral Fellowship in Applied Mathematics.

• Postdoctoral fellow in theoretical and computational neuroscience at the Swartz Program

• Postdoctoral fellow in reinforcement learning and control at Prof Na Li’s lab.

• Postdoctoral positions at Harvard Data Science initiative

• Center for Research on Computation and Society (CRCS) Phd fellowship and postdoc position

• Postdoc position in reinforcement learning at MIT with Sasha Rakhlin

• Postdoc position in high dimensional statistics and machine learning with Pragya Sur (Harvard statistics)

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