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How Machine Learning won the Higgs Boson Challenge

Claire Adam-Bourdarios 1, 2 Glen Cowan 3 Cécile Germain 1, 4 Isabelle Guyon 1, 5 Balázs Kégl 1, 2, 4 David Rousseau 1, 2 
4 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : In 2014 we ran a very successful machine learning challenge in High Ern-ergy physics attracting 1785 teams, which exposed the machine learning community for the first time to the problem of " learning to discover " ( While physicists had the opportunity to improve on the state-of-the-art using " feature engineering " based on physics principles, this was not the determining factor in winning the challenge. Rather, the challenge revealed that the central difficulty of the problem is to develop a strategy to optimize directly the Approximate Median Significance (AMS) objective function, which is a particularly challenging and novel problem. This objective function aims at increasing the power of a statistical test. The top ranking learning machines span a variety of techniques including deep learning and gradient tree boosting. This paper presents the problem setting and analyzes the results.
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Submitted on : Wednesday, December 28, 2016 - 2:22:02 PM
Last modification on : Friday, November 18, 2022 - 9:23:18 AM
Long-term archiving on: : Monday, March 20, 2017 - 6:17:55 PM


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  • HAL Id : hal-01423097, version 1


Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, et al.. How Machine Learning won the Higgs Boson Challenge. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2016, Bruges, Belgium. ⟨hal-01423097⟩



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