Corelab Seminar

Dimitris Tsipras
Robust Machine Learning: The Worst-Case and Beyond

One of the key challenges in the real-world deployment of machine learning (ML) models is their brittleness: their performance significantly degrades when exposed to even small variations of their training environments.

How can we build ML models that are more robust?

In this talk, I will present a methodology for training models that are invariant to a broad family of worst-case input perturbations. I will then describe how such robust learning leads to models that learn fundamentally different data representations, and how this can be useful even outside the adversarial context. Finally, I will discuss model robustness beyond the worst-case: ways in which our models fail to generalize and how we can guide further progress on this front.