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ORCIDGilles Louppe
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- affiliation: University of Liège, Belgium
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2020 – today
- 2024
[j9]Norman Marlier
, Olivier Brüls, Gilles Louppe:
Grasping Under Uncertainties: Sequential Neural Ratio Estimation for 6-DoF Robotic Grasping. IEEE Robotics Autom. Lett. 9(8): 7063-7069 (2024)- [c30]François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe:
Learning Diffusion Priors from Observations by Expectation Maximization. NeurIPS 2024 - [i55]François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe:
Learning Diffusion Priors from Observations by Expectation Maximization. CoRR abs/2405.13712 (2024) - [i54]Arnaud Delaunoy, Maxence de la Brassinne Bonardeaux, Siddharth Mishra-Sharma, Gilles Louppe:
Low-Budget Simulation-Based Inference with Bayesian Neural Networks. CoRR abs/2408.15136 (2024) - [i53]Omer Rochman Sharabi, Sacha Lewin, Gilles Louppe:
A Neural Material Point Method for Particle-based Simulations. CoRR abs/2408.15753 (2024) - [i52]Matthias Pirlet, Adrien Bolland, Gilles Louppe, Damien Ernst:
Costs Estimation in Unit Commitment Problems using Simulation-Based Inference. CoRR abs/2409.03588 (2024) - [i51]Franciszek Szewczyk, Gilles Louppe, Matthia Sabatelli:
Video-Driven Graph Network-Based Simulators. CoRR abs/2409.15344 (2024) - 2023
- [j8]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) - [j7]Thibaut Théate, Antoine Wehenkel, Adrien Bolland, Gilles Louppe, Damien Ernst:
Distributional reinforcement learning with unconstrained monotonic neural networks. Neurocomputing 534: 199-219 (2023) - [j6]Adrien Bolland, Gilles Louppe, Damien Ernst:
Policy Gradient Algorithms Implicitly Optimize by Continuation. Trans. Mach. Learn. Res. 2023 (2023) - [j5]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) - [c29]Renaud Vandeghen, Gilles Louppe, Marc Van Droogenbroeck:
Adaptive Self-Training for Object Detection. ICCV (Workshops) 2023: 914-923 - [c28]Sacha Lewin, Maxime Vandegar, Thomas Hoyoux, Olivier Barnich, Gilles Louppe:
Dynamic NeRFs for Soccer Scenes. MMSports@MM 2023: 113-121 - [c27]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 - [c26]François Rozet, Gilles Louppe:
Score-based Data Assimilation. NeurIPS 2023 - [i50]Norman Marlier, Olivier Brüls, Gilles Louppe:
Simulation-based Bayesian inference for robotic grasping. CoRR abs/2303.05873 (2023) - [i49]Namid R. Stillman, Silke Henkes, Roberto Mayor, Gilles Louppe:
Graph-informed simulation-based inference for models of active matter. CoRR abs/2304.06806 (2023) - [i48]Norman Marlier, Julien Gustin, Olivier Brüls, Gilles Louppe:
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping. CoRR abs/2304.08805 (2023) - [i47]Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe:
Balancing Simulation-based Inference for Conservative Posteriors. CoRR abs/2304.10978 (2023) - [i46]Adrien Bolland, Gilles Louppe, Damien Ernst:
Policy Gradient Algorithms Implicitly Optimize by Continuation. CoRR abs/2305.06851 (2023) - [i45]François Rozet, Gilles Louppe:
Score-based Data Assimilation. CoRR abs/2306.10574 (2023) - [i44]Sacha Lewin, Maxime Vandegar, Thomas Hoyoux, Olivier Barnich, Gilles Louppe:
Dynamic NeRFs for Soccer Scenes. CoRR abs/2309.06802 (2023) - [i43]François Rozet, Gilles Louppe:
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model. CoRR abs/2310.01853 (2023) - [i42]Victor Mangeleer, Gilles Louppe:
Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators. CoRR abs/2310.02691 (2023) - [i41]Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis:
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. CoRR abs/2310.13402 (2023) - [i40]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 abs/2311.00775 (2023) - 2022
- [j4]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) - [c25]Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe:
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. NeurIPS 2022 - [i39]Arnaud Delaunoy, Gilles Louppe:
SAE: Sequential Anchored Ensembles. CoRR abs/2201.00649 (2022) - [i38]Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen:
Robust Hybrid Learning With Expert Augmentation. CoRR abs/2202.03881 (2022) - [i37]Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe:
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. CoRR abs/2208.13624 (2022) - [i36]Renaud Vandeghen, Gilles Louppe, Marc Van Droogenbroeck:
Adaptive Self-Training for Object Detection. CoRR abs/2212.05911 (2022) - 2021
- [c24]Antoine Wehenkel, Gilles Louppe:
Graphical Normalizing Flows. AISTATS 2021: 37-45 - [c23]Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe:
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. AISTATS 2021: 2107-2115 - [c22]Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger:
Truncated Marginal Neural Ratio Estimation. NeurIPS 2021: 129-143 - [c21]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: 3533-3543 - [c20]Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort:
HNPE: Leveraging Global Parameters for Neural Posterior Estimation. NeurIPS 2021: 13432-13443 - [i35]Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort:
Leveraging Global Parameters for Flow-based Neural Posterior Estimation. CoRR abs/2102.06477 (2021) - [i34]Thibaut Théate, Antoine Wehenkel, Adrien Bolland, Gilles Louppe, Damien Ernst:
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks. CoRR abs/2106.03228 (2021) - [i33]Antoine Wehenkel, Gilles Louppe:
Diffusion Priors In Variational Autoencoders. CoRR abs/2106.15671 (2021) - [i32]Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger:
Truncated Marginal Neural Ratio Estimation. CoRR abs/2107.01214 (2021) - [i31]Norman Marlier, Olivier Brüls, Gilles Louppe:
Simulation-based Bayesian inference for multi-fingered robotic grasping. CoRR abs/2109.14275 (2021) - [i30]François Rozet, Gilles Louppe:
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference. CoRR abs/2110.00449 (2021) - [i29]Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Gilles Louppe:
Averting A Crisis In Simulation-Based Inference. CoRR abs/2110.06581 (2021) - [i28]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 abs/2111.02218 (2021) - 2020
- [c19]Joeri Hermans, Volodimir Begy, Gilles Louppe:
Likelihood-free MCMC with Amortized Approximate Ratio Estimators. ICML 2020: 4239-4248 - [c18]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. IJCNN 2020: 1-8 - [e2]Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe:
Artificial Intelligence and Machine Learning - 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers. Communications in Computer and Information Science 1196, Springer 2020, ISBN 978-3-030-65153-4 [contents] - [i27]Antoine Wehenkel, Gilles Louppe:
You say Normalizing Flows I see Bayesian Networks. CoRR abs/2006.00866 (2020) - [i26]Antoine Wehenkel, Gilles Louppe:
Graphical Normalizing Flows. CoRR abs/2006.02548 (2020) - [i25]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 abs/2010.12931 (2020) - [i24]Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe:
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. CoRR abs/2011.05836 (2020) - [i23]Benjamin Kurt Miller, Alex Cole, Gilles Louppe, Christoph Weniger:
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time. CoRR abs/2011.13951 (2020) - [i22]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 abs/2012.12062 (2020)
2010 – 2019
- 2019
- [c17]Gilles Louppe, Joeri Hermans, Kyle Cranmer:
Adversarial Variational Optimization of Non-Differentiable Simulators. AISTATS 2019: 1438-1447 - [c16]Gilles Louppe, Joeri Hermans, Kyle Cranmer:
Adversarial Variational Optimization of Non-Differentiable Simulators. BNAIC/BENELEARN 2019 - [c15]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Deep Quality-Value (DQV) Learning. BNAIC/BENELEARN 2019 - [c14]Antoine Wehenkel, Gilles Louppe:
Unconstrained Monotonic Neural Networks. BNAIC/BENELEARN 2019 - [c13]Antoine Wehenkel, Gilles Louppe:
Unconstrained Monotonic Neural Networks. NeurIPS 2019: 1543-1553 - [c12]Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas 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: 5460-5473 - [c11]Atilim Günes 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 H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC 2019: 29:1-29:24 - [e1]Katrien Beuls, Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe, Paul Van Eecke:
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019. CEUR Workshop Proceedings 2491, CEUR-WS.org 2019 [contents] - [i21]Joeri Hermans, Volodimir Begy, Gilles Louppe:
Likelihood-free MCMC with Approximate Likelihood Ratios. CoRR abs/1903.04057 (2019) - [i20]Atilim Günes 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 H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR abs/1907.03382 (2019) - [i19]Antoine Wehenkel, Gilles Louppe:
Unconstrained Monotonic Neural Networks. CoRR abs/1908.05164 (2019) - [i18]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 abs/1909.01779 (2019) - [i17]Kyle Cranmer, Johann Brehmer, Gilles Louppe:
The frontier of simulation-based inference. CoRR abs/1911.01429 (2019) - 2018
- [c10]Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts:
Random Subspace with Trees for Feature Selection Under Memory Constraints. AISTATS 2018: 929-937 - [i16]Joeri Hermans, Gilles Louppe:
Gradient Energy Matching for Distributed Asynchronous Gradient Descent. CoRR abs/1805.08469 (2018) - [i15]Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer:
Mining gold from implicit models to improve likelihood-free inference. CoRR abs/1805.12244 (2018) - [i14]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 V. 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, 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 S. 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 Chatham 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 abs/1807.02876 (2018) - [i13]Atilim Gunes Baydin, Lukas 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 abs/1807.07706 (2018) - [i12]Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer:
Likelihood-free inference with an improved cross-entropy estimator. CoRR abs/1808.00973 (2018) - [i11]Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering:
Deep Quality-Value (DQV) Learning. CoRR abs/1810.00368 (2018) - [i10]Arthur Pesah, Antoine Wehenkel, Gilles Louppe:
Recurrent machines for likelihood-free inference. CoRR abs/1811.12932 (2018) - 2017
- [c9]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. NIPS 2017: 981-990 - [i9]Gilles Louppe, Kyle Cranmer:
Adversarial Variational Optimization of Non-Differentiable Simulators. CoRR abs/1707.07113 (2017) - [i8]Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts:
Random Subspace with Trees for Feature Selection Under Memory Constraints. CoRR abs/1709.01177 (2017) - [i7]Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat:
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. CoRR abs/1712.07901 (2017) - 2016
- [j3]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): 1395-1401 (2016) - [j2]Gilles Louppe, Kyle Cranmer, Juan Pavez:
carl: a likelihood-free inference toolbox. J. Open Source Softw. 1(1): 11 (2016) - [c8]Gilles Louppe, Hussein T. Al-Natsheh, Mateusz Susik, Eamonn James Maguire:
Ethnicity Sensitive Author Disambiguation Using Semi-supervised Learning. KESW 2016: 272-287 - [c7]Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
Context-dependent feature analysis with random forests. UAI 2016 - [c6]Eamonn Maguire, Javier Martin Montull, Gilles Louppe:
Visualization of Publication Impact. EuroVis (Short Papers) 2016: 103-107 - [i6]Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts:
Context-dependent feature analysis with random forests. CoRR abs/1605.03848 (2016) - [i5]Eamonn James Maguire, Javier Martin Montull, Gilles Louppe:
Visualization of Publication Impact. CoRR abs/1605.06242 (2016) - [i4]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. CoRR abs/1611.01046 (2016) - 2015
- [j1]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): 29-33 (2015) - [i3]Gilles Louppe, Hussein T. Al-Natsheh, Mateusz Susik, Eamonn James Maguire:
Ethnicity sensitive author disambiguation using semi-supervised learning. CoRR abs/1508.07744 (2015) - 2014
- [b1]Gilles Louppe:
Understanding Random Forests: From Theory to Practice. University of Liège, Belgium, 2014 - [c5]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: 23-34 - [c4]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: 902-906 - [i2]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 abs/1406.7865 (2014) - 2013
- [c3]Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts:
Understanding variable importances in forests of randomized trees. NIPS 2013: 431-439 - [i1]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 abs/1309.0238 (2013) - 2012
- [c2]Gilles Louppe, Pierre Geurts:
Ensembles on Random Patches. ECML/PKDD (1) 2012: 346-361 - 2011
- [c1]Pierre Geurts, Gilles Louppe:
Learning to rank with extremely randomized trees. Yahoo! Learning to Rank Challenge 2011: 49-61
Coauthor Index
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