6-9 March 2017
Europe/Zurich timezone

Young Scientist Forum : Weakly supervised classifiers in High Energy Physics

9 Mar 2017, 10:45


11 : Using tracks


Francesco Rubbo (SLAC National Accelerator Laboratory (US))


As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach to classification called weak supervision in which class proportions are the only input into the machine learning algorithm. A simple and general regularization technique is used to solve this non-convex problem. Using one of the most important binary classification tasks in high energy physics - quark versus gluon tagging - we show that weak supervision can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weak supervision is a general procedure that could be applied to a variety of learning problems and as such could add robustness to a wide range of learning problems.

Primary authors

Ariel Gustavo Schwartzman (SLAC National Accelerator Laboratory (US)) Ben Nachman (Lawrence Berkeley National Lab. (US)) Francesco Rubbo (SLAC National Accelerator Laboratory (US)) Lucio Dery (Stanford University)

Presentation Materials