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See detailAdversarial Variational Optimization of Non-Differentiable Simulators
Louppe, Gilles ULiege; Hermans, Joeri ULiege; Cranmer, Kyle

in Proceedings of Machine Learning Research (2019, April)

Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations. Inference in this setup is often ... [more ▼]

Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations. Inference in this setup is often difficult, as simulators rarely admit a tractable density or likelihood function. We introduce Adversarial Variational Optimization (AVO), a likelihood-free inference algorithm for fitting a non-differentiable generative model incorporating ideas from generative adversarial networks, variational optimization and empirical Bayes. We adapt the training procedure of Wasserstein GANs by replacing the differentiable generative network with a domain-specific simulator. We solve the resulting non-differentiable minimax problem by minimizing variational upper bounds of the two adversarial objectives. Effectively, the procedure results in learning a proposal distribution over simulator parameters, such that the Wasserstein distance between the marginal distribution of the synthetic data and the empirical distribution of observed data is minimized. We present results of the method with simulators producing both discrete and continuous data. [less ▲]

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See detailQCD-Aware Recursive Neural Networks for Jet Physics
Louppe, Gilles ULiege; Cho, Kyunghyun; Becot, Cyril et al

in Journal of High Energy Physics (2019)

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built ... [more ▼]

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering history of sequential recombination jet algorithms is like the parsing of a sentence. Our approach works directly with the four-momenta of a variable-length set of particles, and the jet-based tree structure varies on an event-by-event basis. Our experiments highlight the flexibility of our method for building task-specific jet embeddings and show that recursive architectures are significantly more accurate and data efficient than previous image-based networks. We extend the analogy from individual jets (sentences) to full events (paragraphs), and show for the first time an event-level classifier operating on all the stable particles produced in an LHC event. [less ▲]

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See detailConstraining Effective Field Theories with Machine Learning
Brehmer, Johann; Cranmer, Kyle; Louppe, Gilles ULiege et al

in Physical Review Letters (2018)

We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra information from Monte-Carlo ... [more ▼]

We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra information from Monte-Carlo simulations, which can be used to train neural network models that estimate the likelihood ratio. These methods scale well to processes with many observables and theory parameters, do not require any approximations of the parton shower or detector response, and can be evaluated in microseconds. We show that they allow us to put significantly stronger bounds on dimension-six operators than existing methods, demonstrating their potential to improve the precision of the LHC legacy constraints. [less ▲]

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See detailA Guide to Constraining Effective Field Theories with Machine Learning
Brehmer, Johann; Cranmer, Kyle; Louppe, Gilles ULiege et al

in Physical Review. D. (2018)

We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract ... [more ▼]

We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator. This augmented data can be used to train neural networks that precisely estimate the likelihood ratio. The new methods scale well to many observables and high-dimensional parameter spaces, do not require any approximations of the parton shower and detector response, and can be evaluated in microseconds. Using weak-boson-fusion Higgs production as an example process, we compare the performance of several techniques. The best results are found for likelihood ratio estimators trained with extra information about the score, the gradient of the log likelihood function with respect to the theory parameters. The score also provides sufficient statistics that contain all the information needed for inference in the neighborhood of the Standard Model. These methods enable us to put significantly stronger bounds on effective dimension-six operators than the traditional approach based on histograms. They also outperform generic machine learning methods that do not make use of the particle physics structure, demonstrating their potential to substantially improve the new physics reach of the LHC legacy results. [less ▲]

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See detailLikelihood-free inference with an improved cross-entropy estimator
Stoye, Markus; Brehmer, Johann; Louppe, Gilles ULiege et al

E-print/Working paper (2018)

We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joint likelihood ratio and ... [more ▼]

We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joint likelihood ratio and joint score, conditioned on both observed and latent variables, can often be extracted from an implicit generative model or simulator to augment the training data for these surrogate models. We show how this augmented training data can be used to provide a new cross-entropy estimator, which provides improved sample efficiency compared to previous loss functions exploiting this augmented training data. [less ▲]

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See detailEfficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Gunes Baydin, Atilim; Heinrich, Lukas; Bhimji, Wahid et al

E-print/Working paper (2018)

We present a novel framework that enables efficient probabilistic inference in large-scale scientific models by allowing the execution of existing domain-specific simulators as probabilistic programs ... [more ▼]

We present a novel framework that enables efficient probabilistic inference in large-scale scientific models by allowing the execution of existing domain-specific simulators as probabilistic programs, resulting in highly interpretable posterior inference. Our framework is general purpose and scalable, and is based on a cross-platform probabilistic execution protocol through which an inference engine can control simulators in a language-agnostic way. 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. High-energy physics has a rich set of simulators based on quantum field theory and the interaction of particles in matter. We show how to use probabilistic programming to perform Bayesian inference in these existing simulator codebases directly, in particular conditioning on observable outputs from a simulated particle detector to directly produce an interpretable posterior distribution over decay pathways. 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 Markov chain Monte Carlo sampling. [less ▲]

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See detailMachine Learning in High Energy Physics Community White Paper
Albertsson, Kim; Altoe, Piero; Anderson, Dustin et al

in Journal of Physics. Conference Series (2018), 1085

Machine learning is an important research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle ... [more ▼]

Machine learning is an important research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit. [less ▲]

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See detailNew approaches using machine learning for fast shower simulation in ATLAS
Hasib, Ahmed; Schaarschmidt, Jana; Gadatsch, Stefan et al

Conference (2018, July 05)

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See detailMining gold from implicit models to improve likelihood-free inference
Brehmer, Johann; Louppe, Gilles ULiege; Pavez, Juan et al

E-print/Working paper (2018)

Simulators often provide the best description of real-world phenomena; however, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a ... [more ▼]

Simulators often provide the best description of real-world phenomena; however, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods. [less ▲]

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See detailImprovements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Lezcano Casado, Mario; Gunes Baydin, Atilim; Martinez Rubio, David et al

E-print/Working paper (2017)

We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to ... [more ▼]

We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level mechanistic components are composed to create complex generative models. These models lead to intractable likelihoods and are typically non-differentiable, which poses challenges for traditional approaches to inference. We extend previous work in "inference compilation", which combines universal probabilistic programming and deep learning methods, to large-scale scientific simulators, and introduce a C++ based probabilistic programming library called CPProb. We successfully use CPProb to interface with SHERPA, a large code-base used in particle physics. Here we describe the technical innovations realized and planned for this library. [less ▲]

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See detailNeural Message Passing for Jet Physics
Henrion, Isaac; Brehmer, Johann; Bruna, Joan et al

Conference (2017, December 08)

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See detailNeural Message Passing for Jet Physics
Henrion, Isaac; Brehmer, Johann; Bruna, Joan et al

E-print/Working paper (2017)

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See detailAdversarial Variational Optimization of Non-Differentiable Simulators
Louppe, Gilles ULiege; Cranmer, Kyle

Conference (2017, July 20)

Detailed reference viewed: 14 (1 ULiège)
See detailQCD-Aware Recursive Neural Networks for Jet Physics
Louppe, Gilles ULiege; Cho, Kyunghyun; Becot, Cyril et al

Conference (2017, June 07)

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See detailLearning to pivot with adversarial networks
Louppe, Gilles ULiege; Kagan, Michael; Cranmer, Kyle

Conference (2017, May 10)

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See detailLearning to pivot with adversarial networks
Louppe, Gilles ULiege; Kagan, Michael; Cranmer, Kyle

Conference (2017, April 07)

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See detailLearning to pivot with adversarial networks
Louppe, Gilles ULiege; Kagan, Michael; Cranmer, Kyle

Conference (2016, November 17)

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See detailLearning to Pivot with Adversarial Networks
Louppe, Gilles ULiege; Kagan, Michael; Cranmer, Kyle

in Advances in Neural Information Processing Systems (2016, November 03)

Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing. The majority of this work focuses on a binary domain ... [more ▼]

Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing. The majority of this work focuses on a binary domain label. Similar problems occur in a scientific context where there may be a continuous family of plausible data generation processes associated to the presence of systematic uncertainties. Robust inference is possible if it is based on a pivot -- a quantity whose distribution does not depend on the unknown values of the nuisance parameters that parametrize this family of data generation processes. In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property (or, equivalently, fairness with respect to continuous attributes) on a predictive model. The method includes a hyperparameter to control the trade-off between accuracy and robustness. We demonstrate the effectiveness of this approach with a toy example and examples from particle physics. [less ▲]

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See detailApproximating likelihood ratios with Calibrated Classifiers
Louppe, Gilles ULiege; Cranmer, Kyle; Pavez, Juan

Conference (2016, June 22)

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See detailApproximating likelihood ratios with Calibrated Classifiers
Louppe, Gilles ULiege; Cranmer, Kyle; Pavez, Juan

Conference (2016, March 29)

Detailed reference viewed: 18 (3 ULiège)