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Editors: Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau
Preface
; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:i-v
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Real-time data analysis at the LHC: present and future
Vladimir Gligorov; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:1-18
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The Higgs boson machine learning challenge
Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balàzs Kégl, David Rousseau; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:19-55
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Dissecting the Winning Solution of the HiggsML Challenge
Gábor Melis; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:57-67
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Higgs Boson Discovery with Boosted Trees
Tianqi Chen, Tong He; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:69-80
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Deep Learning, Dark Knowledge, and Dark Matter
Peter Sadowski, Julian Collado, Daniel Whiteson, Pierre Baldi; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:81-87
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Consistent optimization of AMS by logistic loss minimization
Wojciech Kotłowski; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:99-108
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Optimization of AMS using Weighted AUC optimized models
Roberto Díaz-Morales, Ángel Navia-Vázquez; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:109-127
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Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge
Lester Mackey, Jordan Bryan, Man Yue Mo; Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:129-134
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