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Poster
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atilim Gunes Baydin · Lei Shao · Wahid Bhimji · Lukas Heinrich · Saeid Naderiparizi · Andreas Munk · Jialin Liu · Bradley Gram-Hansen · Gilles Louppe · Lawrence Meadows · Philip Torr · Victor Lee · Kyle Cranmer · Mr. Prabhat · Frank Wood

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #155
in Probabilistic Methods -- MCMC »

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.

Keywords: MCMC · Probabilistic Methods · Algorithms -> Uncertainty Estimation; Applications; Probabilistic Methods
[ Paper]  [ Slides] 

Author Information

Atilim Gunes Baydin (University of Oxford)
Lei Shao (Intel Corporation)
Wahid Bhimji (Berkeley lab)
Lukas Heinrich (New York University)
Saeid Naderiparizi (University of British Columbia)
Andreas Munk (University of British Columbia)
Jialin Liu (Lawrence Berkeley National Lab)
Bradley Gram-Hansen (University of Oxford)

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Gilles Louppe (University of Liège)
Lawrence Meadows (Intel Corporation)
Philip Torr (University of Oxford)
Victor Lee (Intel Corporation)
Kyle Cranmer (New York University)

Kyle Cranmer is an Associate Professor of Physics at New York University and affiliated with NYU's Center for Data Science. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July, 2012. His current interests are at the intersection of physics and machine learning and include inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, adversarial training for robustness to systematic uncertainty, the use of generative models in the physical sciences, and integration of reproducible workflows in the inference pipeline.

Mr. Prabhat (LBL/NERSC)
Frank Wood (University of British Columbia)

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