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HEPML workshop at NIPS14

December 13, 2014
Palais des Congrès de Montréal
Europe/Paris timezone

Contribution List

13 out of 13 displayed
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  1. 0. Welcome
    Balázs Kégl (LAL)
    12/13/14, 8:30 AM
  2. 1. HEP&ML and the HiggsML challenge
    Balázs Kégl (LAL)
    12/13/14, 8:45 AM
    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.
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  3. 2. Embedding ML in Classical Statistical tests used in HEP (invited talk)
    Kyle Cranmer (New York University)
    12/13/14, 9:20 AM
    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.
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  4. 3. Presentation of the winner of the HiggsML challenge
    Gábor Melis
    12/13/14, 10:30 AM
    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.
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  5. 4. Presentation of the runner up of the HiggsML challenge
    Tim Salimans
    12/13/14, 10:50 AM
    High Energy Physics provides a challenging data domain with data that is highly structured, but also very noisy. I will present what I have learned analyzing this data for the HiggsML challenge, focusing on methods that are able to effectively search through a high dimensional model space while also achieving good statistical efficiency. In addition, I will discuss the role of the physicist in...
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  6. 5. Presentation of the winner of the HEP meets ML prize
    Tianqi Chen
    12/13/14, 11:10 AM
    In this talk, I will describe how we use principle of gradient boosting method to construct simple and effective regression trees functions for Higgs Boson detection. We take a functional space optimization framework that jointly optimize the training objective and simplicity of functions learnt. I talk about how the objective could be clearly related to the tree searching, pruning and leave...
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  7. 6. Real time data analysis at the LHC : present and future
    Vava Gligorov
    12/13/14, 11:30 AM
    The large hadron collider (LHC), which collides protons at an energy of 14 TeV (for non-physicists, each beam of protons carries roughly the energy of a TGV train going at full speed), produces hundreds of exabytes of data per year, making it one of the largest sources of data in the world today. At present it is not possible to even transfer most of this data from the four main particle...
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  8. 7. Machine Learning for Ultra-High-Energy Physics (invited talk)
    Daniel Whiteson
    12/13/14, 3:00 PM
    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/
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  9. 9. Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge
    Lester Mackey
    12/13/14, 3:40 PM
    We introduce a minorization-maximization approach to optimizing common measures of discovery significance in high energy physics. The approach alternates between solving a weighted binary classification problem and updating class weights in a simple, closed-form manner. Moreover, an argument based on convex duality shows that an improvement in weighted classification error on any round...
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  10. 8. Consistent optimization of AMS by logistic loss minimization
    Wojciech Kotlowski
    12/13/14, 4:00 PM
    In this paper, we theoretically justify an approach popular among participants of the Higgs Boson Machine Learning Challenge to optimize approximate median significance (AMS). The approach is based on the following two-stage procedure. First, a real-valued function is learned by minimizing a surrogate loss for binary classification, such as logistic loss, on the training sample. Then, a...
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  11. 10. Ensemble of maximied Weighted AUC models for the maximization of the median discovery significance
    Roberto Diaz Morales (University Carlos III de Madrid)
    12/13/14, 5:00 PM
    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.
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  12. 11. Deep Learning In High-Energy Physics (invited talk)
    Pierre Baldi
    12/13/14, 5:20 PM
    We will provide a brief overview of the challenges and opportunities facing machine learning in the natural sciences, from physics to biology, and then focus on the application of deep learning methods to problems in high-energy physics. In particular we will describe the results obtained on three different problems (Higgs boson detection, Supersymmetry, and Higgs boson decay).
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  13. 12. Panel discussion
    12/13/14, 6:00 PM

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