Weakly Supervised Classification for High Energy Physics

Abstract

We present a classification algorithm that applies the machine learning paradigm of Learning from Label Proportions (LLP) [1] to enable learning on unlabelled data. Our algorithm, Weakly Supervised Classification, receives as its only input the class proportions of batches of data but makes per-instance classification decisions matching the performance of fully supervised approaches. We apply our model to the problem of Quark-Gluon tagging and show that it is robust to underlying mismodelling of the simulated data unlike fully supervised learning.