13 décembre 2014
Palais des Congrès de Montréal
Fuseau horaire Europe/Paris

Session

Session 1

13 déc. 2014, 08:30
Level 5, room 511 c (Palais des Congrès de Montréal)

Level 5, room 511 c

Palais des Congrès de Montréal

Documents de présentation

Aucun document.

  1. Balázs Kégl (LAL)
    13/12/2014 08:30
  2. 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.
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  3. 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.
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