21-25 August 2017
University of Washington, Seattle
US/Pacific timezone

Weakly Supervised Classifiers in High Energy Physics

24 Aug 2017, 16:00
The Commons (Alder Hall)

The Commons

Alder Hall

Poster Track 2: Data Analysis - Algorithms and Tools Poster Session


Lucio Dery (Stanford)


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.

Primary authors

Francesco Rubbo (SLAC National Accelerator Laboratory (US)) Ben Nachman (Lawrence Berkeley National Lab. (US)) Lucio Dery (Stanford) Ariel Gustavo Schwartzman (SLAC National Accelerator Laboratory (US)) Eric Metodiev (MIT) Patrick Komiske (Massachusetts Institute of Technology) Matthew Schwartz

Presentation materials

Peer reviewing