Title: Weakly Supervised Classification in High Energy Physics,
Abstract: 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 called weakly supervised classification in
which class proportions are the only input into the machine learning algorithm.
Using one of the most challenging binary classification tasks in high energy
physics - quark versus gluon tagging - we show that weakly supervised
classification 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. Weakly supervised
classification is a general procedure that can be applied to a wide variety of
learning problems to boost performance and robustness when detailed simulations
are not reliable or not available.