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Gilles Louppe

Averting A Crisis In Simulation-Based Inference


Oct 13, 2021
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Gilles Louppe

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Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference


Oct 01, 2021
François Rozet, Gilles Louppe

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* 4 pages, 3 figures, submitted to the Machine Learning and the Physical Sciences workshop at NeurIPS 2021 
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Simulation-based Bayesian inference for multi-fingered robotic grasping


Sep 29, 2021
Norman Marlier, Olivier Brüls, Gilles Louppe

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Truncated Marginal Neural Ratio Estimation


Jul 02, 2021
Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger

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* 9 pages. 23 pages with references and supplemental material. Code available at http://github.com/bkmi/tmnre/ Underlying library http://github.com/undark-lab/swyft/ 
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Diffusion Priors In Variational Autoencoders


Jun 29, 2021
Antoine Wehenkel, Gilles Louppe

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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

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* 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

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* 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

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* 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

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* 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

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* 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

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* 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

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* 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

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


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

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* 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

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* 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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