Welcome to the website for the 2022 Princeton Machine Learning Theory Summer School. The school will run in person June 13 to June 17, 2022 and is aimed at PhD students interested in machine learning theory. The primary goal is to showcase, through four main courses, a range of exciting recent developments in the subject. The primary focus this year is on theoretical advances in deep learning. An important secondary goal is to connect young researchers and foster a closer community within theoretical machine learning.

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Spherical representation of a neural network.


There will be four principle courses, each consisting of four to five hours of lecture. The courses are:

  1. Implicit Complexity Control in Deep and Underdetermined Models
    Instructor: Nati Srebro (TTIC)
  2. Graph Neural Networks and Equivariant Machine Learning
    Instructor: Soledad Villar (JHU)
  3. The Law of Robustness, a Story of Small and Large Neural Networks
    Instructor: Sebastien Bubeck (MSR)
  4. Understanding Self-supervised Learning with Neural Networks
    Instructor: Tengyu Ma (Stanford)

Lecture Schedule


PhD students in any technical discipline with a strong interest in theory are encouraged to apply. Accepted participants will be given free acommodation (double occupancy) in Princeton and will be eligible for a travel reimbursement of up to $500. Applications require a CV, a letter of recommendation and a statement of purpose, which can all be submitted here. Full consideration will be given to applications completed by March 31, 2022.

Math on a chalkboard.

Organizers and Sponsors

This summer school is organized by Boris Hanin (Princeton ORFE ). Support was provided by the NSF via NSF CAREER Grant DMS-2143754, the Department of Operations Research and Financial Engineering (ORFE) at Princeton, the Center for Statistics and Machine Learning (CSML) at Princeton, the Princeton School of Engineering and Applied Sciences (SEAS), and the Program on Applied and Computational Mathematics (PACM) at Princeton.

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