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Gilles Louppe
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning

Dec 22, 2020
Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli

* To be published in AAAI-21 Workshop on Reinforcement Learning in Games 
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Towards constraining warm dark matter with stellar streams through neural simulation-based inference

Nov 30, 2020
Joeri Hermans, Nilanjan Banik, Christoph Weniger, Gianfranco Bertone, Gilles Louppe


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Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time

Nov 27, 2020
Benjamin Kurt Miller, Alex Cole, Gilles Louppe, Christoph Weniger

* Accepted at Machine Learning and the Physical Sciences at NeurIPS 2020. Package: https://github.com/undark-lab/swyft/ 
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Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization

Nov 12, 2020
Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer, Samaya Nissanke, Christoph Weniger, Andrew R. Williamson, Gilles Louppe

* V1: First version; V2: Updated references; V3: Update references and camera-ready version 
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Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

Nov 11, 2020
Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe


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Graphical Normalizing Flows

Jun 03, 2020
Antoine Wehenkel, Gilles Louppe


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You say Normalizing Flows I see Bayesian Networks

Jun 03, 2020
Antoine Wehenkel, Gilles Louppe


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The frontier of simulation-based inference

Nov 14, 2019
Kyle Cranmer, Johann Brehmer, Gilles Louppe

* v2 fixed typos. 8 pages, 3 figures, proceedings for the Sackler Colloquia at the US National Academy of Sciences 
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Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

Oct 17, 2019
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, Kyle Cranmer

* 23 pages, 6 figures, code available at https://github.com/smsharma/mining-for-substructure-lens; v2, minor changes to text, version accepted in ApJ 
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Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms

Sep 01, 2019
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering


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Unconstrained Monotonic Neural Networks

Aug 14, 2019
Antoine Wehenkel, Gilles Louppe


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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Jul 08, 2019
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip Torr, Victor Lee, Kyle Cranmer, Prabhat, Frank Wood

* 14 pages, 8 figures 
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Effective LHC measurements with matrix elements and machine learning

Jun 04, 2019
Johann Brehmer, Kyle Cranmer, Irina Espejo, Felix Kling, Gilles Louppe, Juan Pavez

* Keynote at the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019) 
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Likelihood-free MCMC with Approximate Likelihood Ratios

Mar 10, 2019
Joeri Hermans, Volodimir Begy, Gilles Louppe

* 13 pages, 10 figures 
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Recurrent machines for likelihood-free inference

Nov 30, 2018
Arthur Pesah, Antoine Wehenkel, Gilles Louppe

* NeurIPS 2018 Workshop on Meta-learning (MetaLearn 2018) 
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Deep Quality-Value (DQV) Learning

Oct 10, 2018
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering


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Mining gold from implicit models to improve likelihood-free inference

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 
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Adversarial Variational Optimization of Non-Differentiable Simulators

Oct 05, 2018
Gilles Louppe, Joeri Hermans, Kyle Cranmer


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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

* 18 pages, 5 figures 
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Likelihood-free inference with an improved cross-entropy estimator

Aug 02, 2018
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

* 8 pages, 3 figures 
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A Guide to Constraining Effective Field Theories with Machine Learning

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 
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Constraining Effective Field Theories with Machine Learning

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 
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QCD-Aware Recursive Neural Networks for Jet Physics

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 
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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

* Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm 
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Scikit-learn: Machine Learning in Python

Jun 05, 2018
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay

* Journal of Machine Learning Research (2011) 
 * Update authors list and URLs 
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