Gilles Louppe

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 80

Top Topics

Reinforcement Learning

Bayesian Inference

Top Venues

BNAIC/BENELEARN

Publications

François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe
Learning Diffusion Priors from Observations by Expectation Maximization. CoRR (2024)
Norman Marlier, Olivier Brüls, Gilles Louppe
Grasping Under Uncertainties: Sequential Neural Ratio Estimation for 6-DoF Robotic Grasping. IEEE Robotics Autom. Lett. 9 (8) (2024)
Adrien Bolland, Gilles Louppe, Damien Ernst
Policy Gradient Algorithms Implicitly Optimize by Continuation. Trans. Mach. Learn. Res. 2023 (2023)
Adrien Bolland, Gilles Louppe, Damien Ernst
Policy Gradient Algorithms Implicitly Optimize by Continuation. CoRR (2023)
Namid R. Stillman, Silke Henkes, Roberto Mayor, Gilles Louppe
Graph-informed simulation-based inference for models of active matter. CoRR (2023)
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen
Robust Hybrid Learning With Expert Augmentation. Trans. Mach. Learn. Res. 2023 (2023)
Victor Mangeleer, Gilles Louppe
Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators. CoRR (2023)
Marc Joiret, Marine Leclercq, Gaspard Lambrechts, Francesca Rapino, Pierre Close, Gilles Louppe, Liesbet Geris
Cracking the genetic code with neural networks. Frontiers Artif. Intell. 6 (2023)
Sacha Lewin, Maxime Vandegar, Thomas Hoyoux, Olivier Barnich, Gilles Louppe
Dynamic NeRFs for Soccer Scenes. CoRR (2023)
Sacha Lewin, Maxime Vandegar, Thomas Hoyoux, Olivier Barnich, Gilles Louppe
Dynamic NeRFs for Soccer Scenes. MMSports@MM (2023)
Thibaut Théate, Antoine Wehenkel, Adrien Bolland, Gilles Louppe, Damien Ernst
Distributional reinforcement learning with unconstrained monotonic neural networks. Neurocomputing 534 (2023)
Norman Marlier, Julien Gustin, Olivier Brüls, Gilles Louppe
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping. CoRR (2023)
Renaud Vandeghen, Gilles Louppe, Marc Van Droogenbroeck
Adaptive Self-Training for Object Detection. ICCV (Workshops) (2023)
Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. NeurIPS (2023)
Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe
Balancing Simulation-based Inference for Conservative Posteriors. CoRR (2023)
François Rozet, Gilles Louppe
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model. CoRR (2023)
Norman Marlier, Olivier Brüls, Gilles Louppe
Simulation-based Bayesian inference for robotic grasping. CoRR (2023)
Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. CoRR (2023)
Aaron David Schneider, Paul Mollière, Gilles Louppe, Ludmila Carone, Uffe Gråe Jørgensen, Leen Decin, Christiane Helling
Harnessing machine learning for accurate treatment of overlapping opacity species in GCMs. CoRR (2023)
François Rozet, Gilles Louppe
Score-based Data Assimilation. NeurIPS (2023)
François Rozet, Gilles Louppe
Score-based Data Assimilation. CoRR (2023)
Arnaud Delaunoy, Gilles Louppe
SAE: Sequential Anchored Ensembles. CoRR (2022)
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen
Robust Hybrid Learning With Expert Augmentation. CoRR (2022)
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Volodimir Begy, Gilles Louppe
A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful. Trans. Mach. Learn. Res. 2022 (2022)
Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. NeurIPS (2022)
Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. CoRR (2022)
Renaud Vandeghen, Gilles Louppe, Marc Van Droogenbroeck
Adaptive Self-Training for Object Detection. CoRR (2022)
Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
From global to local MDI variable importances for random forests and when they are Shapley values. NeurIPS (2021)
Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
From global to local MDI variable importances for random forests and when they are Shapley values. CoRR (2021)
Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
Truncated Marginal Neural Ratio Estimation. NeurIPS (2021)
Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort
HNPE: Leveraging Global Parameters for Neural Posterior Estimation. NeurIPS (2021)
François Rozet, Gilles Louppe
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference. CoRR (2021)
Norman Marlier, Olivier Brüls, Gilles Louppe
Simulation-based Bayesian inference for multi-fingered robotic grasping. CoRR (2021)
Antoine Wehenkel, Gilles Louppe
Diffusion Priors In Variational Autoencoders. CoRR (2021)
Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. AISTATS (2021)
Antoine Wehenkel, Gilles Louppe
Graphical Normalizing Flows. AISTATS (2021)
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Gilles Louppe
Averting A Crisis In Simulation-Based Inference. CoRR (2021)
Thibaut Théate, Antoine Wehenkel, Adrien Bolland, Gilles Louppe, Damien Ernst
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks. CoRR (2021)
Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort
Leveraging Global Parameters for Flow-based Neural Posterior Estimation. CoRR (2021)
Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
Truncated Marginal Neural Ratio Estimation. CoRR (2021)
Antoine Wehenkel, Gilles Louppe
Graphical Normalizing Flows. CoRR (2020)
Antoine Wehenkel, Gilles Louppe
You say Normalizing Flows I see Bayesian Networks. CoRR (2020)
Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. CoRR (2020)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. IJCNN (2020)
Joeri Hermans, Volodimir Begy, Gilles Louppe
Likelihood-free MCMC with Amortized Approximate Ratio Estimators. ICML (2020)
Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer, Samaya Nissanke, Christoph Weniger, Andrew R. Williamson, Gilles Louppe
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization. CoRR (2020)
Benjamin Kurt Miller, Alex Cole, Gilles Louppe, Christoph Weniger
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time. CoRR (2020)
Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. CoRR (2020)
Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR (2019)
Antoine Wehenkel, Gilles Louppe
Unconstrained Monotonic Neural Networks. NeurIPS (2019)
Joeri Hermans, Volodimir Begy, Gilles Louppe
Likelihood-free MCMC with Approximate Likelihood Ratios. CoRR (2019)
Gilles Louppe, Joeri Hermans, Kyle Cranmer
Adversarial Variational Optimization of Non-Differentiable Simulators. AISTATS (2019)
Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC (2019)
Antoine Wehenkel, Gilles Louppe
Unconstrained Monotonic Neural Networks. CoRR (2019)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Deep Quality-Value (DQV) Learning. BNAIC/BENELEARN (2019)
Gilles Louppe, Joeri Hermans, Kyle Cranmer
Adversarial Variational Optimization of Non-Differentiable Simulators. BNAIC/BENELEARN (2019)
Kyle Cranmer, Johann Brehmer, Gilles Louppe
The frontier of simulation-based inference. CoRR (2019)
Antoine Wehenkel, Gilles Louppe
Unconstrained Monotonic Neural Networks. BNAIC/BENELEARN (2019)
Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Alexander Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIPS (2019)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. CoRR (2019)
Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Deep Quality-Value (DQV) Learning. CoRR (2018)
Atilim Gunes Baydin, Lukas Alexander Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. CoRR (2018)
Joeri Hermans, Gilles Louppe
Gradient Energy Matching for Distributed Asynchronous Gradient Descent. CoRR (2018)
Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts
Random Subspace with Trees for Feature Selection Under Memory Constraints. AISTATS (2018)
Arthur Pesah, Antoine Wehenkel, Gilles Louppe
Recurrent machines for likelihood-free inference. CoRR (2018)
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Likelihood-free inference with an improved cross-entropy estimator. CoRR (2018)
Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Mining gold from implicit models to improve likelihood-free inference. CoRR (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 M. 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 Alexander Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, 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 B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. 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 Andrew 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 in High Energy Physics Community White Paper. CoRR (2018)
Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Alexander Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR (2017)
Gilles Louppe, Michael Kagan, Kyle Cranmer
Learning to Pivot with Adversarial Networks. NIPS (2017)
Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts
Random Subspace with Trees for Feature Selection Under Memory Constraints. CoRR (2017)
Gilles Louppe, Kyle Cranmer
Adversarial Variational Optimization of Non-Differentiable Simulators. CoRR (2017)
Gilles Louppe, Michael Kagan, Kyle Cranmer
Learning to Pivot with Adversarial Networks. CoRR (2016)
Gilles Louppe, Hussein T. Al-Natsheh, Mateusz Susik, Eamonn James Maguire
Ethnicity Sensitive Author Disambiguation Using Semi-supervised Learning. KESW (2016)
Gilles Louppe, Kyle Cranmer, Juan Pavez
carl: a likelihood-free inference toolbox. J. Open Source Softw. 1 (1) (2016)
Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
Context-dependent feature analysis with random forests. UAI (2016)
Eamonn James Maguire, Javier Martin Montull, Gilles Louppe
Visualization of Publication Impact. CoRR (2016)
Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
Context-dependent feature analysis with random forests. CoRR (2016)
Eamonn Maguire, Javier Martin Montull, Gilles Louppe
Visualization of Publication Impact. EuroVis (Short Papers) (2016)
Raphaël Marée, Loic Rollus, Benjamin Stevens, Renaud Hoyoux, Gilles Louppe, Remy Vandaele, Jean-Michel Begon, Philipp Kainz, Pierre Geurts, Louis Wehenkel
Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinform. 32 (9) (2016)
Gilles Louppe, Hussein T. Al-Natsheh, Mateusz Susik, Eamonn James Maguire
Ethnicity sensitive author disambiguation using semi-supervised learning. CoRR (2015)
Gaël Varoquaux, Lars Buitinck, Gilles Louppe, Olivier Grisel, Fabian Pedregosa, Andreas Mueller
Scikit-learn: Machine Learning Without Learning the Machinery. GetMobile Mob. Comput. Commun. 19 (1) (2015)
Raphaël Marée, Loic Rollus, Benjamin Stevens, Gilles Louppe, Olivier Caubo, Natacha Rocks, Sandrine Bekaert, Didier Cataldo, Louis Wehenkel
A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning. ISBI (2014)
Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts
Simple connectome inference from partial correlation statistics in calcium imaging. CoRR (2014)
Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts
Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging. Neural Connectomics (2014)
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, Gaël Varoquaux
API design for machine learning software: experiences from the scikit-learn project. CoRR (2013)
Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
Understanding variable importances in forests of randomized trees. NIPS (2013)
Gilles Louppe, Pierre Geurts
Ensembles on Random Patches. ECML/PKDD (1) (2012)
Pierre Geurts, Gilles Louppe
Learning to rank with extremely randomized trees. Yahoo! Learning to Rank Challenge (2011)