Volume 42: NIPS 2014 Workshop on High-energy Physics and Machine Learning, 13 December 2014, Montreal, Canada
[edit]
Editors:
Glen Cowan,
Cécile Germain,
Isabelle Guyon,
Balázs Kégl,
David Rousseau
[bib][citeproc]
Preface
Accepted Papers
Real-time data analysis at the LHC: present and future
Vladimir Gligorov
;
PMLR 42:1-18
[abs]
[Download PDF]
The Higgs boson machine learning challenge
Claire Adam-Bourdarios,
Glen Cowan,
Cécile Germain,
Isabelle Guyon,
Balàzs Kégl,
David Rousseau
;
PMLR 42:19-55
[abs]
[Download PDF]
Dissecting the Winning Solution of the HiggsML Challenge
Gábor Melis
;
PMLR 42:57-67
[abs]
[Download PDF]
Higgs Boson Discovery with Boosted Trees
Tianqi Chen,
Tong He
;
PMLR 42:69-80
[abs]
[Download PDF]
Deep Learning, Dark Knowledge, and Dark Matter
Peter Sadowski,
Julian Collado,
Daniel Whiteson,
Pierre Baldi
;
PMLR 42:81-87
[abs]
[Download PDF]
Consistent optimization of AMS by logistic loss minimization
Wojciech Kotłowski
;
PMLR 42:99-108
[abs]
[Download PDF]
Optimization of AMS using Weighted AUC optimized models
Roberto Díaz-Morales,
Ángel Navia-Vázquez
;
PMLR 42:109-127
[abs]
[Download PDF]
Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML
Challenge
Lester Mackey,
Jordan Bryan,
Man Yue Mo
;
PMLR 42:129-134
[abs]
[Download PDF]
