In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics. In this talk, we will present a new framework: JUNIPR, Jets from UNsupervised Interpretable PRobabilistic models, which uses unsupervised learning to learn the intricate high-dimensional contours of the data upon which it is trained, without reference to pre-established labels. In order to approach such a complex task, JUNIPR is structured intelligently around a leading-order model of the physics underlying the data. In addition to making unsupervised learning tractable, this design actually alleviates existing tensions between performance and interpretability. Applications to discrimination, data-driven Monte Carlo generation and reweighting of events will be discussed. Full details about this meeting will be posted here: https://www.benty-fields.com/manage_jc?groupid=191.
The Berkeley Statistics and Machine Learning Forum meets weekly to discuss current applications across a wide variety of research domains and software methodologies. Register here to view, propose and vote for this group's upcoming discussion topics. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. Questions may be directed to François Lanusse.