Robustness in Machine Learning (CSE 599-M)

Course description

As machine learning is applied to increasingly sensitive tasks, and applied on noisier and noisier data, it has become important that the algorithms we develop for ML are robust to potentially worst-case noise. In this class, we will survey a number of recent developments in the study of robust machine learning, from both a theoretical and empirical perspective. Tentatively, we will cover a number of related topics, both theoretical and applied, including:

Our goal (though we will often fall short of this task) is to devise theoretically sound algorithms for these tasks which transfer well to practice.

The intended audience for this class is CS graduate students in Theoretical Computer Science and/or Machine Learning, who are interested in doing research in this area. However, interested undergraduates and students from other departments are welcome to attend as well. The coursework will be light and consist of some short problem sets as well as a final project.

For non-CSE students/undergraduates: If you are interested in this class, please attend the first lecture. If the material suits your interests and background, please request an add code from me afterwards.


We will assume mathematical maturity and comfort with algorithms, probability, and linear algebra. Background in machine learning will be helpful but should not be necessary.

Project Proposals
Please turn in (or email) a one page project proposal by November 12th. Projects can be reading projects, where you survey the literature on some area that we didn't cover, or research projects, where you try (but not necessarily succeed at) tackling an open problem in the area. Projects can be either theoretical or applied. If you're short of ideas, please feel free to ask the instructor!
Homework problems will be added as we cover the appropriate material. Each homework will be due one to two weeks after we finish the corresponding unit.
Lecture notes are work in progress. Feedback is welcome!

Supplementary material
Please refer to university policies regarding disability accommodations or religious accommodations.