Nikhef Colloquium: "Teaching machines to discover particles"

Friday, 29 September 2017 from to (Europe/Amsterdam)
at Nikhef ( H331 )
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  • Friday, 29 September 2017
    • 11:00 - 12:00 Teaching machines to discover particles 1h0'
      Abstract: Across many fields of science, computer simulators are used to describe complex
      data generation processes. These simulators relate observations to the
      parameters of an underlying theory or mechanistic model. In most cases, they are
      specified as procedural implementations of forward stochastic processes
      involving latent variables. Rarely do these simulators admit a tractable density
      or likelihood function, thereby making inference difficult. The prevalence and
      significance of this problem has motivated an active research effort in
      so-called likelihood-free inference algorithms.
      
      In this talk, we will discuss how likelihood-free inference has been carried for
      decades in particle physics and then expand to new methods from machine
      learning, including likelihood-ratio estimation through supervised learning [1]
      and adversarial variational optimization [2].
      
      Building upon these algorithms, we will then discuss how artificial intelligence
      can enable the automation of the scientific method through the synergy of
      three powerful techniques:
      - Generic likelihood-free inference engines that enable statistical
        inference​ ​on​ the​ ​ parameters​​ of​ a​ theory​​ that​​ are​  implicitly​ ​
        defined​ ​by​ a​ simulator.
      - Sequential design algorithms (e.g., Bayesian optimization) that balance
        exploration and exploitation​ ​ to​ ​ efficiently​ ​ optimize​ ​ an​ ​ expensive​ ​
        black​ ​ box​ ​ function.
      - Workflows that encapsulate scientific pipelines and extend the scope
        from reproducibility to​​ reusability.​
      
      [1] https://arxiv.org/abs/1506.02169
      [2] https://arxiv.org/abs/1707.07113
      
      Speaker: Dr. Gilles Louppe (NYU)
      Material: Slides pdf file