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Featured researches published by Cécile Germain.


Archive | 2014

Learning to discover: the Higgs boson machine learning challenge

Claire Adam-Bourdarios; Glen Cowan; Cécile Germain; Isabelle Guyon; Balázs Kégl; D. Rousseau

Preamble The Higgs boson machine learning challenge (HiggsML or Challenge in short) has been set up to promote collaboration between high energy physicists and data scientists. The ATLAS experiment at CERN provided simulated data used by physicists to optimize the analysis of the Higgs boson. The Challenge is organized by a small group of ATLAS physicists and data scientists. It is hosted by Kaggle at https://www.kaggle.com/c/higgs-boson. This document provides technical background for the Challenge; reading and understanding it is not necessary to participate in the Challenge, but it is probably useful. No prior knowledge of high energy physics is required. All information on how to participate in the Challenge is available on the Kaggle web site.


neural information processing systems | 2014

The Higgs boson machine learning challenge

Claire Adam-Bourdarios; Glen Cowan; Cécile Germain; Isabelle Guyon; Balázs Kégl; D. Rousseau


the european symposium on artificial neural networks | 2016

How Machine Learning won the Higgs Boson Challenge

Claire Adam-Bourdarios; Glen Cowan; Cécile Germain; Isabelle Guyon; Balázs Kégl; D. Rousseau


neural information processing systems | 2016

The Tracking Machine Learning Challenge

D. Rousseau; Cécile Germain; V. Innocente; Riccardo Cenci; Michael Kagan; Isabelle Guyon; David Clark; Steve Farrel; Rebecca Carney; A. Salzburger; Davide Costanzo; M. Elsing; T. Golling; Tony Tong; Jean-Roch Vlimant; Sandro Wenzel


the european symposium on artificial neural networks | 2018

Systematics aware learning: a case study in High Energy Physics

Victor Estrade; Cécile Germain; Isabelle Guyon; D. Rousseau


neural information processing systems | 2018

The TrackML challenge

D. Rousseau; Sabrina Amrouche; P. Calafiura; S. Farrell; Cécile Germain; V. V. Gligorov; T. Golling; Heather Gray; Isabelle Guyon; M. Hushchyn; V. Innocente; Moritz Kiehn; A. Salzburger; A. Ustyuzhanin; Jean-Roch Vlimant; Yetkin Yilmaz


Archive | 2018

WCCI 2018 TrackML Particle Tracking Challenge

D. Rousseau; Sabrina Amrouche; P. Calafiura; S. Farrell; Cécile Germain; V. V. Gligorov; T. Golling; Heather Gray; Isabelle Guyon; M. Hushchyn; V. Innocente; Moritz Kiehn; A. Salzburger; A. Ustyuzhanin; Jean-Roch Vlimant; Yetkin Yilmaz


neural information processing systems | 2017

Adversarial learning to eliminate systematic errors: a case study in High Energy Physics

Victor Estrade; Cécile Germain; Isabelle Guyon; D. Rousseau


The European physical journal / Web of Conferences | 2017

Track reconstruction at LHC as a collaborative data challenge use case with RAMP

Sabrina Amrouche; Nils Braun; P. Calafiura; S. Farrell; Jochen Gemmler; Cécile Germain; V. V. Gligorov; T. Golling; Heather Gray; Isabelle Guyon; M. Hushchyn; V. Innocente; Balázs Kégl; Sara Neuhaus; D. Rousseau; A. Salzburger; Andrei Ustyuzhanin; Jean-Roch Vlimant; Christian Wessel; Yetkin Yilmaz


Archive | 2017

Tracking at LHC as a collaborative data challenge use case with RAMP

Sabrina Amrouche; T. Golling; V. Innocente; P. Calafiura; Balázs Kégl; Heather Gray; Yetkin Yilmaz; Cécile Germain; V. V. Gligorov; D. Rousseau; Andrei Ustyuzhanin; Jean-Roch Vlimant; Nils Braun; A. Salzburger; Jochen Gammler; Sara Neuhaus; Christian Wessel; Hadrien Grasland; Isabelle Guyon; S. Farrell; M. Hushchyn

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D. Rousseau

Université Paris-Saclay

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Isabelle Guyon

Université Paris-Saclay

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Balázs Kégl

Université Paris-Saclay

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Isabelle Guyon

Université Paris-Saclay

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Jean-Roch Vlimant

California Institute of Technology

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Claire Adam-Bourdarios

Centre national de la recherche scientifique

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