Preface

The Editors; 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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