Speaker
Jesse Thaler
(MIT/IAIFI)
Description
The term "interpretability" encompasses various strategies to scrutinize the decisions made by machine learning algorithms. In this talk, I argue that interpretability, at least in the context of particle physics, should be considered as part of the broader goal of assessing systematic uncertainties. I provide examples from my own research on jet physics at the Large Hadron Collider, where some of the goals of interpretability can be achieved through modified machine learning architectures and training paradigms. I also comment on the growing interest in foundation models, which are forcing us to rethink how we specify machine learning tasks and whether there can be statistical gains from incorporating auxiliary information.
