Publications and communications of Louppe, Gilles

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

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

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

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

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

Wehenkel, A., Behrmann, J., Hsu, H., Sapiro, G., Louppe, G., & Jacobsen, J.-H. (2022). Robust Hybrid Learning With Expert Augmentation. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/290899.

Delaunoy, A., & Louppe, G. (14 December 2021). SAE: Sequential Anchored Ensembles. Paper presented at Bayesian Deep Learning, NeurIPS 2021 workshop.

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

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 presented at BNAIC/BeneLearn 2021, Esch-sur-Alzette, Luxembourg.

Hermans, J., Delaunoy, A., Rozet, F., Wehenkel, A., & Louppe, G. (2021). Averting A Crisis In Simulation-Based Inference. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/265148.

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. Paper presented at 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 presented at 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 presented at EuroHPC's LUMI kick-off, Belgium.

Marlier, N., Bruls, O., & Louppe, G. (2021). Simulation-based Bayesian inference for multi-fingered robotic grasping. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/266100.

Théate, T., Wehenkel, A., Bolland, A., Louppe, G., & Ernst, D. (2021). Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/260785.

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. Paper presented at 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. Paper presented at 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. Paper presented at 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 presented at Parietal Seminar Series, Paris, France.

Wehenkel, A., & Louppe, G. (10 July 2020). You say Normalizing Flows I see Bayesian Networks. Paper presented at 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 session presented at 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. Paper presented at 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 session presented at 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

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 session presented at Bayesian Deep Learning, NeurIPS 2018 Workshop, Montreal, Canada.

Pesah, A., Wehenkel, A., & Louppe, G. (08 December 2018). Recurrent machines for likelihood-free inference. Paper presented at 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. (2018). Likelihood-free inference with an improved cross-entropy estimator. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/227022.

The ATLAS collaboration, & Louppe, G. (Other coll.). (2018). Deep generative models for fast shower simulation in ATLAS. Eprint/Working paper retrieved from https://orbi.uliege.be/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. Eprint/Working paper retrieved from https://orbi.uliege.be/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 session presented at 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. Eprint/Working paper retrieved from https://orbi.uliege.be/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. Paper presented at 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. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/226016.

Louppe, G. (03 April 2015). Tree models with Scikit-Learn: Great models with little assumptions. Paper presented at 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 presented at Paris Machine Learning Meetup 4 (saison 2), Paris, France.

Louppe, G. (18 November 2014). Scikit-Learn in Particle Physics. Paper presented at 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. Unpublished doctoral thesis, ULiège - Université de Liège.
Jury: Geurts, P. (Promotor), Wehenkel, L., Boigelot, B., Detry, R., Bontempi, G., & Biau, G.

Louppe, G. (29 August 2014). Accelerating Random Forests in Scikit-Learn. Paper presented at 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 presented at PyData 2014, London, United Kingdom.

Louppe, G., & Prettenhofer, P. (05 February 2014). Forecasting Daily Solar Energy Production Using Robust Regression Techniques. Paper presented at 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 session presented at GIGA DAY 2014, Liège, Belgium.

Louppe, G., & Varoquaux, G. (10 December 2013). Scikit-Learn: Machine Learning in the Python ecosystem. Paper presented at 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 presented at 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. Unpublished master thesis, ULiège - Université de Liège.
Jury: Geurts, P. (Promotor), Boigelot, B., Sepulchre, R., Wehenkel, L., & Leduc, G.