* 11 pages, 9 figures, 2 tables. Source code available at https://github.com/dexgen/backdrop

**Click to Read Paper**

Adversarial Variational Optimization of Non-Differentiable Simulators

Oct 05, 2018

Gilles Louppe, Joeri Hermans, Kyle Cranmer

Oct 05, 2018

Gilles Louppe, Joeri Hermans, Kyle Cranmer

**Click to Read Paper**

* v1: Original submission. v2: Fixed references. v3: version submitted to NIPS'2017. Code available at https://github.com/glouppe/paper-learning-to-pivot

**Click to Read Paper**

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

Mar 18, 2016

Kyle Cranmer, Juan Pavez, Gilles Louppe

In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters $\theta$ of an underlying theory and measurement apparatus to high-dimensional observations $\mathbf{x}\in \mathbb{R}^p$. However, simulator often do not provide a way to evaluate the likelihood function for a given observation $\mathbf{x}$, which motivates a new class of likelihood-free inference algorithms. In this paper, we show that likelihood ratios are invariant under a specific class of dimensionality reduction maps $\mathbb{R}^p \mapsto \mathbb{R}$. As a direct consequence, we show that discriminative classifiers can be used to approximate the generalized likelihood ratio statistic when only a generative model for the data is available. This leads to a new machine learning-based approach to likelihood-free inference that is complementary to Approximate Bayesian Computation, and which does not require a prior on the model parameters. Experimental results on artificial problems with known exact likelihoods illustrate the potential of the proposed method.
Mar 18, 2016

Kyle Cranmer, Juan Pavez, Gilles Louppe

* 35 pages, 5 figures

**Click to Read Paper**

Mining gold from implicit models to improve likelihood-free inference

Oct 09, 2018

Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

Oct 09, 2018

Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

* Code available at https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos. v3: Expanded discussion, added Lotka-Volterra example

**Click to Read Paper**

A Guide to Constraining Effective Field Theories with Machine Learning

Jul 26, 2018

Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

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.
Jul 26, 2018

Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

* Phys. Rev. D 98, 052004 (2018)

* See also the companion publication "Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00013, a brief introduction presenting the key ideas. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: Added references. v3: Improved description of algorithms, added references. v4: Clarified text, added references

**Click to Read Paper**

Constraining Effective Field Theories with Machine Learning

Jul 26, 2018

Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

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.
Jul 26, 2018

Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

* Phys. Rev. Lett. 121, 111801 (2018)

* See also the companion publication "A Guide to Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00020, an in-depth analysis of machine learning techniques for LHC measurements. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: New schematic figure explaining the new algorithms, added references. v3, v4: Added references

**Click to Read Paper**

QCD-Aware Recursive Neural Networks for Jet Physics

Jul 13, 2018

Gilles Louppe, Kyunghyun Cho, Cyril Becot, Kyle Cranmer

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.
Jul 13, 2018

Gilles Louppe, Kyunghyun Cho, Cyril Becot, Kyle Cranmer

* 16 pages, 5 figures, 3 appendices, corresponding code at https://github.com/glouppe/recnn

**Click to Read Paper**

Likelihood-free inference with an improved cross-entropy estimator

Aug 02, 2018

Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

Aug 02, 2018

Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

* 8 pages, 3 figures

**Click to Read Paper**

Parameterized Machine Learning for High-Energy Physics

Jan 28, 2016

Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson

Jan 28, 2016

Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson

* For submission to PRD

**Click to Read Paper**

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

Sep 01, 2018

Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank Wood

Sep 01, 2018

Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank Wood

* 18 pages, 5 figures

**Click to Read Paper**

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

Dec 21, 2017

Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

Dec 21, 2017

Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

* 7 pages, 2 figures

**Click to Read Paper**

Machine Learning in High Energy Physics Community White Paper

Jul 08, 2018

Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

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.
Jul 08, 2018

Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

* Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

**Click to Read Paper**