Teaching machines to discover particles

• Gilles Louppe (NYU)

Room: H331 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