Publications and communications of Gilles Louppe

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (02 April 2024). Neural network-based simulation of fields and losses in electrical machines with ferromagnetic laminated cores. International Journal of Numerical Modelling, 37 (2).

Rochman Sharabi, O., & Louppe, G. (15 December 2023). Trick or treat? Evaluating stability strategies in graph network-based simulators [Poster presentation]. Machine Learning and the Physical Sciences, NeurIPS 2023.

Falkiewicz, M., Takeishi, N., Shekhzadeh, I., Wehenkel, A., Delaunoy, A., Louppe, G., & Kalousis, A. (2023). Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. Advances in Neural Information Processing Systems. doi:10.48550/arXiv.2310.13402

David Schneider, A., Mollière, P., Louppe, G., Carone, L., Gråe Jørgensen, U., Decin, L., & Helling, C. (2023). Harnessing machine learning for accurate treatment of overlapping opacity species in GCMs. Astronomy and Astrophysics. doi:10.1051/0004-6361/202348221

Mangeleer, V., & Louppe, G. (01 November 2023). Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators [Poster presentation]. Machine Learning and the Physical Sciences, NeurIPS 2023, New Orleans, United States.

Bolland, A., Louppe, G., & Ernst, D. (2023). Policy Gradient Algorithms Implicitly Optimize by Continuation. Transactions on Machine Learning Research.

Vandeghen, R., Louppe, G., & Van Droogenbroeck, M. (October 2023). Adaptive Self-Training for Object Detection [Poster presentation]. IEEE/CVF International Conference on Computer Vision Workshops (ICCV Workshops), Paris, France. doi:10.1109/ICCVW60793.2023.00098

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (04 September 2023). Fast and accurate Neural-Network-based Ferromagnetic Laminated Stack Model for Electrical Machine Simulations in Periodic Regime [Paper presentation]. COMPUMAG 2023, Kyoto, Japan.

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (30 August 2023). Neural-Network-Based Identification of Material Law Parameters for Fast and Accurate Simulations of Electrical Machines in Periodic Regime [Poster presentation]. EMF 2023, Marseille, France.

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (12 July 2023). Neural-Network-Based Identification of Material Law Parameters for Fast and Accurate Simulations of Electrical Machines in Periodic Regime [Paper presentation]. EMF 2023, Marseille, France.

Bolland, A., Louppe, G., & Ernst, D. (19 June 2023). Policy Gradient Algorithms Implicitly Optimize by Continuation [Poster presentation]. ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, Honolulu, United States - Hawaii.

Marlier, N., Gustin, J., Brüls, O., & Louppe, G. (02 June 2023). Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping [Paper presentation]. ICRA 2023, Londres, United Kingdom.

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (26 May 2023). Fast and accurate Neural-Network-based Ferromagnetic Laminated Stack Model for Electrical Machine Simulations in Periodic Regime [Paper presentation]. COMPUMAG 2023, Kyoto, Japan.

Théate, T., Wehenkel, A., Bolland, A., Louppe, G., & Ernst, D. (14 May 2023). Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks. Neurocomputing, 534, 199-219. doi:10.1016/j.neucom.2023.02.049

Stillman, N. R., Henkes, S., Mayor, R., & Louppe, G. (May 2023). Graph-informed simulation-based inference for models of active matter [Poster presentation]. ML4Materials workshop @ ICLR 2023.

Delaunoy, A., Kurt Miller, B., Forré, P., Weniger, C., & Louppe, G. (21 April 2023). Balancing Simulation-based Inference for Conservative Posteriors [Paper presentation]. 5th Symposium on Advances in Approximate Bayesian Inference, Honolulu, United States.

Joiret, M., Leclercq, M., Lambrechts, G., Rapino, F., Close, P., Louppe, G., & Geris, L. (06 April 2023). Cracking the genetic code with neural networks. Frontiers in Artificial Intelligence, 6. doi:10.3389/frai.2023.1128153

Vasist, M., Rozet, F., Absil, O., Mollière, P., Nasedkin, E., & Louppe, G. (April 2023). Neural posterior estimation for exoplanetary atmospheric retrieval. Astronomy and Astrophysics, 672, 147. doi:10.1051/0004-6361/202245263

Messina, A., Schyns, M., Dozo, B.-O., Denoël, V., Van Hulle, R., Etienne, A.-M., Delroisse, S., Bruyère, O., D'Orio, V., Fontaine, S., Guillaume, M., Lange, A.-C., Louppe, G., Michel, F., Nyssen, A.-S., Bureau, F., Haubruge, E., Donneau, A.-F., Gillet, L., & Saegerman, C. (2023). Developing a video game as an awareness and research tool based on SARS-CoV-2 epidemiological dynamics and motivational perspectives. Transboundary and Emerging Diseases. doi:10.1155/2023/8205408

Wehenkel, A., Behrmann, J., Hsu, H., Sapiro, G., Louppe, G., & Jacobsen, J.-H. (2023). Robust Hybrid Learning With Expert Augmentation. Transactions on Machine Learning Research.

Joiret, M., Leclercq, M., Lambrechts, G., Rapino, F., Close, P., Louppe, G., & Geris, L. (2023). Cracking the genetic code with neural networks (poster) [Paper presentation]. Neural networks: real versus man-made, Liège, Belgium.

Lewin, S., Vandegar, M., Hoyoux, T., Barnich, O., & Louppe, G. (2023). Dynamic NeRFs for Soccer Scenes [Poster presentation]. 6th International ACM Workshop on Multimedia Content Analysis in Sports, Ottawa, Canada. doi:10.1145/3606038.3616158

Rozet, F., & Louppe, G. (2023). Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model [Poster presentation]. Machine Learning and the Physical Sciences Workshop (NeurIPS 2023), New Orleans, United States - Louisiana.

Rozet, F., & Louppe, G. (2023). Score-based Data Assimilation. Advances in Neural Information Processing Systems.

Rochman Sharabi, O., & Louppe, G. (03 December 2022). Differentiable composition for model discovery [Poster presentation]. Machine Learning and the Physical Sciences, NeurIPS 2022.

Delaunoy, A.* , Hermans, J.* , Rozet, F., Wehenkel, A., & Louppe, G. (2022). Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. Advances in Neural Information Processing Systems.

Quesnel, M., Orban De Xivry, G., Louppe, G., & Absil, O. (01 December 2022). A deep learning approach for focal-plane wavefront sensing using vortex phase diversity. Astronomy and Astrophysics, 668, 36. doi:10.1051/0004-6361/202143001

Hermans, J., Delaunoy, A., Rozet, F., Wehenkel, A., & Louppe, G. (2022). A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful. Transactions on Machine Learning Research.

Marlier, N., Bruls, O., & Louppe, G. (27 October 2022). Simulation-based Bayesian inference for robotic grasping [Paper presentation]. IROS 2022, Kyoto, Japan.

Gal, Y., Koumoutsakos, P., Lanusse, F., Louppe, G., & Papadimitriou, C. (September 2022). Bayesian uncertainty quantification for machine-learned models in physics. Nature Reviews. Physics, 4 (9), 573 - 577. doi:10.1038/s42254-022-00498-4

Quesnel, M., Orban De Xivry, G., Absil, O., & Louppe, G. (2022). A simulator-based autoencoder for focal plane wavefront sensing. In L. Schreiber, D. Schmidt, ... E. Vernet, Adaptive Optics Systems VIII (pp. 1218532). Bellingham, WA, United States: SPIE. doi:10.1117/12.2629476

Louppe, G. (23 May 2022). Simulation-based inference: proceed with caution! [Paper presentation]. DMML group seminars, Genève, Switzerland.

Louppe, G. (17 May 2022). Simulation-based inference: proceed with caution! [Paper presentation]. MINERVA seminars, Paris, France.

Louppe, G. (22 April 2022). Simulation-based inference: proceed with caution! [Paper presentation]. Learning to Discover, Paris, France.

Louppe, G. (21 April 2022). Simulation-based inference: proceed with caution! [Paper presentation]. Likelihood-free in Paris, Paris, France.

Purnode, F., Henrotte, F., Caire, F., Da Silva, J., Louppe, G., & Geuzaine, C. (2022). A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime. IEEE Transactions on Magnetics. doi:10.1109/TMAG.2022.3160651

Denoël, V.* , Bruyère, O.* , Louppe, G., Bureau, F., D'ORIO, V., Fontaine, S., Gillet, L., Guillaume, M., Haubruge, E., Lange, A.-C., Michel, F., Hulle, R. V., Arnst, M., Donneau, A.-F.* , & Saegerman, C.*. (04 March 2022). Decision-based interactive model to determine re-opening conditions of a large university campus in Belgium during the first COVID-19 wave. Archives of Public Health, 80 (1). doi:10.1186/s13690-022-00801-w

Purnode, F., Henrotte, F., Caire François, Da Silva Joaquim, Louppe, G., & Geuzaine, C. (18 January 2022). A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime [Poster presentation]. COMPUMAG 2021.

Delaunoy, A., & Louppe, G. (14 December 2021). SAE: Sequential Anchored Ensembles [Poster presentation]. Bayesian Deep Learning, NeurIPS 2021 workshop.

Rozet, F., & Louppe, G. (13 December 2021). Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference [Poster presentation]. Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021), Vancouver, Canada.

Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2021). From global to local MDI variable importances for random forests and when they are Shapley values. Advances in Neural Information Processing Systems.

Kurt Miller, B., Cole, A., Forré, P., Louppe, G., & Weniger, C. (2021). Truncated Marginal Neural Ratio Estimation. Advances in Neural Information Processing Systems.

Rodrigues, P., Moreau, T., Louppe, G., & Gramfort, A. (2021). HNPE: Leveraging Global Parameters for Neural Posterior Estimation. Advances in Neural Information Processing Systems.

Louppe, G. (11 November 2021). LEGO® Deep Learning [Paper presentation]. BNAIC/BeneLearn 2021, Esch-sur-Alzette, Luxembourg.

Hermans, J., Banik, N., Weniger, C., Bertone, G., & Louppe, G. (2021). Towards constraining warm dark matter with stellar streams through neural simulation-based inference. Monthly Notices of the Royal Astronomical Society. doi:10.1093/mnras/stab2181

Wehenkel, A., & Louppe, G. (July 2021). Diffusion Priors In Variational Autoencoders [Poster presentation]. ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models.

Orban De Xivry, G., Quesnel, M., Vanberg, P.-O., Absil, O., & Louppe, G. (09 June 2021). Focal plane wavefront sensing using machine learning: performance of convolutional neural networks compared to fundamental limits. Monthly Notices of the Royal Astronomical Society, 505 (4), 5702-5713. doi:10.1093/mnras/stab1634

Vandegar, M., Kagan, M., Wehenkel, A., & Louppe, G. (2021). Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. In Proceedings of AISTATS 2021.

Wehenkel, A., & Louppe, G. (2021). Graphical Normalizing Flows. In Proceedings of AISTATS 2021.

Louppe, G. (12 February 2021). The frontier of simulation-based inference [Paper presentation]. AIMS Seminar Series, Oxford, United Kingdom.

Dahlqvist, C.-H., Louppe, G., & Absil, O. (04 February 2021). Improving the RSM map exoplanet detection algorithm - PSF forward modelling and optimal selection of PSF subtraction techniques. Astronomy and Astrophysics, 646, 49. doi:10.1051/0004-6361/202039597

Leroy, P., Ernst, D., Geurts, P., Louppe, G., Pisane, J., & Sabatelli, M. (2021). QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. In Proceedings of the AAAI-21 Workshop on Reinforcement Learning in Games (pp. 8).

Louppe, G. (14 January 2021). Scaling AI for Probabilistic Programming in Scientific Simulators [Paper presentation]. EuroHPC's LUMI kick-off, Belgium.

Marlier, N., Bruls, O., & Louppe, G. (2021). Simulation-based Bayesian inference for multi-fingered robotic grasping. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/266100.

Quesnel, M., Orban De Xivry, G., Louppe, G., & Absil, O. (2020). Deep learning-based focal plane wavefront sensing for classical and coronagraphic imaging. In L. Schreiber, D. Schmidt, ... E. Vernet, Adaptive Optics Systems VII (pp. 114481). Bellingham, WA, United States: SPIE. doi:10.1117/12.2562456

Delaunoy, A., Wehenkel, A., Hinderer, T., Nissanke, S., Weniger, C., Williamson, A., & Louppe, G. (11 December 2020). Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization [Poster presentation]. Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS).

Hermans, J., Banik, N., Weniger, C., Bertone, G., & Louppe, G. (11 December 2020). Probing Dark Matter Substructure with Stellar Streams and Neural Simulation-Based Inference [Poster presentation]. Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS).

Kurt Miller, B., Cole, A., Louppe, G., & Weniger, C. (11 December 2020). Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time [Poster presentation]. Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS).

Louppe, G. (17 November 2020). Likelihood-free MCMC with Amortized Approximate Ratio Estimators [Paper presentation]. Parietal Seminar Series, Paris, France.

Wehenkel, A., & Louppe, G. (10 July 2020). You say Normalizing Flows I see Bayesian Networks [Poster presentation]. ICML2020 Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models.

Hermans, J., Begy, V., & Louppe, G. (2020). Likelihood-free MCMC with Amortized Approximate Ratio Estimators. In Proceedings of the 37th International Conference on Machine Learning (pp. 4239-4248).

Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2020). The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. International Joint Conference on Neural Networks (IJCNN 2020).

Cranmer, K., Brehmer, J., & Louppe, G. (2020). The frontier of simulation-based inference. Proceedings of the National Academy of Sciences of the United States of America. doi:10.1073/pnas.1912789117

Brehmer, J., Louppe, G., Pavez, J., & Cranmer, K. (2020). Mining gold from implicit models to improve likelihood-free inference. Proceedings of the National Academy of Sciences of the United States of America. doi:10.1073/pnas.1915980117

Brehmer, J., Cranmer, K., Mishra-Sharma, S., Kling, F., & Louppe, G. (14 December 2019). Mining gold: Improving simulation-based inference with latent information [Poster presentation]. Machine Learning and the Physical Sciences. Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada.

Vanberg, P.-O., Orban De Xivry, G., Absil, O., & Louppe, G. (14 December 2019). Machine learning for image-based wavefront sensing [Poster presentation]. Machine Learning and the Physical Sciences. Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada.

Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2019). Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. Advances in Neural Information Processing Systems.

Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., & Cranmer, K. (19 November 2019). Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning. Astrophysical Journal, 886 (1). doi:10.3847/1538-4357/ab4c41

Louppe, G., Hermans, J., & Cranmer, K. (08 November 2019). Adversarial Variational Optimization of Non-Differentiable Simulators [Poster presentation]. AI Synergies, Brussels, Belgium.

Wehenkel, A., & Louppe, G. (2019). Unconstrained Monotonic Neural Networks. Advances in Neural Information Processing Systems.

Günes, B. A., Shao, L., Bhimji, W., Heinrich, L., Meadows, L., Liu, J., Munk, A., Naderiparizi, S., Gram-Hansen, B., Louppe, G., Ma, M., Zhao, X., Torr, P., Lee, V., Cranmer, K., Prabhat, & Wood, F. (2019). Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. Proceedings of SC19, 1907.03382. doi:10.1145/3295500.3356180

Gunes Baydin, A., Heinrich, L., Bhimji, W., Gram-Hansen, B., Louppe, G., Shao, L., Prabhat, Cranmer, K., & Wood, F. (2019). Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. Advances in Neural Information Processing Systems.

Brehmer, J., Cranmer, K., Espejo, I., Kling, F., Louppe, G., & Pavez, J. (2019). Effective LHC measurements with matrix elements and machine learning. Proceedings of ACAT 2019.

Louppe, G., Hermans, J., & Cranmer, K. (2019). Adversarial Variational Optimization of Non-Differentiable Simulators. Proceedings of Machine Learning Research.

Louppe, G., Cho, K., Becot, C., & Cranmer, K. (2019). QCD-Aware Recursive Neural Networks for Jet Physics. Journal of High Energy Physics. doi:10.1007/JHEP01(2019)057

Marlier, N., Louppe, G., Bruls, O., & Dislaire, G. (2019). Robotic throwing controller for accelerating a recycling line. In Proceedings of the Robotix Academy Conference for Industrial Robotics (RACIR) 2019. Robotix Academy.

Cranmer, K., Gadatsch, S., Gosh, A., Golling, T., Louppe, G., Rousseau, D., Salamani, D., & Stewart, G. (18 December 2018). Deep generative models for fast shower simulation in ATLAS [Poster presentation]. Bayesian Deep Learning, NeurIPS 2018 Workshop, Montreal, Canada.

Pesah, A., Wehenkel, A., & Louppe, G. (08 December 2018). Recurrent machines for likelihood-free inference [Poster presentation]. Workshop of Meta-Learning at Thirty-second Conference on Neural Information Processing Systems 2018, Montreal, Canada.

Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2018). Deep Quality Value (DQV) Learning. Advances in Neural Information Processing Systems.

Brehmer, J., Cranmer, K., Louppe, G., & Pavez, J. (2018). Constraining Effective Field Theories with Machine Learning. Physical Review Letters. doi:10.1103/PhysRevLett.121.111801

Brehmer, J., Cranmer, K., Louppe, G., & Pavez, J. (2018). A Guide to Constraining Effective Field Theories with Machine Learning. Physical Review. D. doi:10.1103/PhysRevD.98.052004

Stoye, M., Brehmer, J., Louppe, G., Pavez, J., & Cranmer, K. (02 August 2018). Likelihood-free inference with an improved cross-entropy estimator [Poster presentation]. Machine Learning and the Physical Sciences, NeurIPS 2019, Vancouver, Canada.

The ATLAS collaboration, & Louppe, G. (Other coll.). (2018). Deep generative models for fast shower simulation in ATLAS. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/226551.

Albertsson, K., Altoe, P., Anderson, D., Andrews, M., Araque Espinosa, J. P., Aurisano, A., Basara, L., Bevan, A., Bhimji, W., Bonacorsi, D., Calafiura, P., Campanelli, M., Capps, L., Carminati, F., Carrazza, S., Childers, T., Coniavitis, E., Cranmer, K., David, C., ... Zapata, O. (08 July 2018). Machine Learning in High Energy Physics Community White Paper. Journal of Physics. Conference Series, 1085. doi:10.1088/1742-6596/1085/2/022008

Hermans, J., & Louppe, G. (2018). Gradient Energy Matching for Distributed Asynchronous Gradient Descent. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/226232.

Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (2018). Random Subspace with Trees for Feature Selection Under Memory Constraints. In A. Storkey & F. Perez-Cruz (Eds.), Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (pp. 929-937). Playa Blanca, Spain: PMLR.

Lezcano Casado, M., Gunes Baydin, A., Martinez Rubio, D., Le, T. A., Wood, F., Heinrich, L., Louppe, G., Cranmer, K., Ng, K., Bhimji, W., & Prabhat. (08 December 2017). Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators [Poster presentation]. Deep Learning for Physical Sciences workshop, NeurIPS 2018, Montreal, Canada.

Henrion, I., Brehmer, J., Bruna, J., Cho, K., Cranmer, K., Louppe, G., & Rochette, G. (2017). Neural Message Passing for Jet Physics. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/226446.

Cranmer, K., Pavez, J., Louppe, G., & Brooks, W. K. (2016). Experiments using machine learning to approximate likelihood ratios for mixture models. In Journal of Physics Conference Series. doi:10.1088/1742-6596/762/1/012034

Louppe, G., Kagan, M., & Cranmer, K. (November 2016). Learning to Pivot with Adversarial Networks. Advances in Neural Information Processing Systems, 30.

Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (12 September 2016). Random subspace with trees for feature selection under memory constraints [Poster presentation]. The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium.

Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2016). Context-dependent feature analysis with random forests. In Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016).

Maguire, E., Montull, J. M., & Louppe, G. (2016). Visualization of Publication Impact. In EuroVis '16 Proceedings of the Eurographics / IEEE VGTC Conference on Visualization: Short Papers.

Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.-M., Kainz, P., Geurts, P., & Wehenkel, L. (2016). Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 7. doi:10.1093/bioinformatics/btw013

Louppe, G., Al-Natsheh, H., Susik, M., & Maguire, E. (2015). Ethnicity sensitive author disambiguation using semi-supervised learning. In Communications in Computer and Information Science.

Cranmer, K., Pavez, J., & Louppe, G. (2015). Approximating Likelihood Ratios with Calibrated Discriminative Classifiers. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/226016.

Louppe, G. (03 April 2015). Tree models with Scikit-Learn: Great models with little assumptions [Paper presentation]. PyData Paris 2015, Paris, France.

McGovern, A., Gagne II, D. J., Eustaquio, L., Titericz, G., Lazorthes, B., Zhang, O., Louppe, G., Prettenhofer, P., Basara, J., Hamill, T. M., & Margolin, D. (2015). Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems. Bulletin of the American Meteorological Society. doi:10.1175/BAMS-D-14-00006.1

Louppe, G. (2014). Bias-variance decomposition in Random Forests [Paper presentation]. Paris Machine Learning Meetup 4 (saison 2), Paris, France.

Louppe, G. (18 November 2014). Scikit-Learn in Particle Physics [Paper presentation]. Data Science Academic software: From scikit-learn and scikit-image to domain science, Paris, France.

Louppe, G. (2014). Understanding Random Forests: From Theory to Practice [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/170309

Louppe, G. (29 August 2014). Accelerating Random Forests in Scikit-Learn [Paper presentation]. EuroScipy 2014, Cambridge, United Kingdom.

Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Louppe, G., Ernst, D., & Geurts, P. (2014). Simple connectome inference from partial correlation statistics in calcium imaging. In J. Soriano, D. Battaglia, I. Guyon, V. Lemaire, J. Orlandi, ... B. Ray (Eds.), Neural Connectomics Challenge. Springer.

Marée, R., Rollus, L., Stevens, B., Louppe, G., Caubo, O., Rocks, N., Bekaert, S., Cataldo, D., & Wehenkel, L. (2014). A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning. In Proceedings IEEE International Symposium on Biomedical Imaging. IEEE. doi:10.1109/isbi.2014.6868017

Botta, V., Louppe, G., Geurts, P., & Wehenkel, L. (2014). Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies. PLoS ONE. doi:10.1371/journal.pone.0093379

Prettenhofer, P., & Louppe, G. (23 February 2014). Gradient Boosted Regression Trees in Scikit-Learn [Paper presentation]. PyData 2014, London, United Kingdom.

Louppe, G., & Prettenhofer, P. (05 February 2014). Forecasting Daily Solar Energy Production Using Robust Regression Techniques [Paper presentation]. 94th American Meteorological Society Annual Meeting, Atlanta, United States.

Joly, A., & Louppe, G. (27 January 2014). Scikit-Learn: Machine Learning in the Python ecosystem [Poster presentation]. GIGA DAY 2014, Liège, Belgium.

Louppe, G., & Varoquaux, G. (10 December 2013). Scikit-Learn: Machine Learning in the Python ecosystem [Poster presentation]. NIPS 2013 Workshop on Machine Learning Open Source Software.

Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In Advances in Neural Information Processing Systems 26.

Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F. (Other coll.), Müller, A. (Other coll.), Grisel, O. (Other coll.), Niculae, V. (Other coll.), Prettenhofer, P. (Other coll.), Gramfort, A. (Other coll.), Grobler, J. (Other coll.), Layton, R. (Other coll.), Vanderplas, J. (Other coll.), Joly, A. (Other coll.), Holt, B. (Other coll.), & Varoquaux, G. (Other coll.). (23 September 2013). API design for machine learning software: experiences from the scikit-learn project [Paper presentation]. ECML/PKDD 2013 Workshop: Languages for Data Mining and Machine Learning, Prague, Czechia.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (02 January 2012). Scikit-learn: Machine Learning in Python. arXiv e-prints, 1201.

Louppe, G., & Geurts, P. (2012). Ensembles on Random Patches. In Machine Learning and Knowledge Discovery in Databases. Berlin, Germany: Springer-Verlag.

Geurts, P., & Louppe, G. (January 2011). Learning to rank with extremely randomized trees. Proceedings of Machine Learning Research, 14, 49-61.

Louppe, G. (2010). Collaborative filtering: Scalable approaches using restricted Boltzmann machines [Master’s dissertation, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/74400