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Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks


Jun 06, 2021
Thibaut Théate, Antoine Wehenkel, Adrien Bolland, Gilles Louppe, Damien Ernst


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Leveraging Global Parameters for Flow-based Neural Posterior Estimation


Feb 12, 2021
Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort


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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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