Balázs Kégl
(LAL)
13/12/2014 08:45
We first describe the HiggsML challenge (the problem of optimizing classifiers for discovery significance, the setup of the challenge, the results, and some analysis of the outcome). In the second part we outline some of the application themes of machine learning in high-energy physics.
Kyle Cranmer
(New York University)
13/12/2014 09:20
I will review the ways that machine learning is typically used in particle physics,
some recent advancements, and future directions. In particular, I will
focus on the integration of machine learning and classical statistical procedures.
These considerations motivate a novel construction that is a hybrid of
machine learning algorithms and more traditional likelihood methods.
Gábor Melis
13/12/2014 10:30
We describe the winning solution of the HiggsML challenge, the issues
related to the evaluation metric and reliable assessment of model
performance. Finally, we take a stab at predicting how to achieve
larger improvements.
Daniel Whiteson
13/12/2014 15:00
I will describe the computational and machine learning challenges of the CRAYFIS project: a distributed cosmic ray telescope consisting of consumer smartphones and geared for the detection of ultra-high-energy cosmic rays. For more info: http://crayfis.ps.uci.edu/
Roberto Diaz Morales
(University Carlos III de Madrid)
13/12/2014 17:00
From May 12th 2014 to September 15th 2014 took place the Higgs Boson Machine Learning Challenge. Its goal was to explore machine learning methods to improve the discovery significance of the ATLAS experiment. This talk describes the preprocessing, training and results of our model, that finished in 9th position among the solutions of 1785 teams.
