Speaker
Annalena Kofler
(Technical University Munich)
Description
Discrete decisions arise in simulators and analysis pipelines across disciplines such as biophysics, robotics, and HEP. Because these operations are inherently non-differentiable, the machine learning community has developed a range of methods to obtain such gradients. In this talk, I outline why a statistical perspective on gradient estimation is essential in this setting and give a brief overview of existing approaches for handling discrete decisions.