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.

Arnst, M., Louppe, G., Van Hulle, R., Gillet, L., Bureau, F., & Denoël, V. (May 2022). A hybrid stochastic model and its Bayesian identification for infectious disease screening in a university campus with application to massive COVID-19 screening at the University of Liège. Mathematical Biosciences, 347, 108805. doi:10.1016/j.mbs.2022.108805

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.

Louppe, G. (2022). Une introduction à l'intelligence artificielle. Paper presented at IA et le monde d'aujourd'hui, Liège, Belgium.

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

Louppe, G. (08 February 2022). The frontier of simulation-based inference. Paper presented at Data Learning seminars, London, United Kingdom.

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.

Louppe, G. (05 November 2021). Deep latent variable models. Paper presented at TRAIL doctoral seminars, Liège, Belgium.

Louppe, G. (20 October 2021). A quick tour of deep generative models. Paper presented at Debating the potential of machine learning in astronomical surveys, Paris, France.

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

Louppe, G. (27 July 2021). The frontier of simulation-based inference. Paper presented at Seventh Machine Learning in High Energy Physics Summer School 2021.

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.

Louppe, G. (23 June 2021). Neural Ratio Estimation For Simulation-based Inference. Paper presented at The 28th Nordic Conference in Mathematical Statistics, Tromsø, Norway.

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

Louppe, G. (17 May 2021). The frontier of simulation-based inference. Paper presented at Apprentissage profond et modèles génératifs pour modéliser l'incertitude des données (2e journée), France.

Louppe, G. (07 May 2021). The frontier of simulation-based inference. Paper presented at Algorithms & Computationally Intensive Inference seminars, Warwick, United Kingdom.

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. (26 March 2021). The frontier of simulation-based inference. Paper presented at Applied Statistics Workshop.

Louppe, G. (11 March 2021). The frontier of simulation-based inference. Paper presented at CLARIPHY topical meeting.

Louppe, G. (02 March 2021). Neural Ratio Estimation for Simulation-Based Inference. Paper presented at SIAM Conference on Computational Science and Engineering (CSE21).

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

Louppe, G. (2020). L'intelligence artificielle, danger ou progrès?

Louppe, G. (28 January 2020). A short introduction to Neural Likelihood-free Inference for Physics. Paper presented at AMLD 2020, Lausanne, Switzerland.

Louppe, G. (15 January 2020). LEGO Deep Learning Medium Brick Box. Paper presented at ML4Jets 2020, New York, United States.

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.

Louppe, G. (2019). Artificial Emotional Intelligence. Paper presented at Détection des émotions et intelligence artificielle au service du théâtre, IMPACT forum, Liège, Belgium.

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. (19 November 2019). Neural Likelihood-free Inference. Paper presented at GRAPPA colloquium, Amsterdam, Netherlands.

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

Louppe, G. (31 October 2019). Neural Likelihood-free Inference. Paper presented at Machine Learning and Physics seminars, Oxford, United Kingdom.

Louppe, G. (29 October 2019). Neural Likelihood-free Inference. Paper presented at 1st Pan-European Advanced School on Statistics in High Energy Physics, Hamburg, Germany.

Louppe, G. (21 October 2019). Neural Likelihood-free Inference. Paper presented at PhD AI seminars at BeCentral, Brussels, Belgium.

Louppe, G. (2019). Intelligence artificielle. Paper presented at Conférence APM seniors Liège, Liège, Belgium.

Raimondo, F., Engemann, D., King, J.-R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez Slezak, D., Laureys, S., Naccache, L., Dehaene, S., & Sitt, J. (24 September 2019). Automated Machine Learning-based diagnosis of impaired consciousness: cross-center and protocol generalization of EEG biomarkers. Paper presented at 13th CME International Conference on Complex Medical Engineering, Dortmund, Germany.

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

Louppe, G. (04 July 2019). Likelihood-free inference in Physical Sciences. Paper presented at Fifth Machine Learning in High Energy Physics Summer School, Hamburg, Germany.

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. (2019). L'intelligence artificielle pour une médecine plus humaine.

Louppe, G. (19 April 2019). Likelihood ratio estimation for statistical inference in physical sciences. Paper presented at NTT Machine Learning workshop, Kyoto, Japan.

Louppe, G. (08 April 2019). Lectures on Deep Learning. Paper presented at Advanced Workshop on Accelerating the Search for Dark Matter with Machine Learning, Trieste, Italy.

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

Louppe, G. (22 March 2019). Likelihood-free inference in Physical Sciences. Paper presented at Artificial Intelligence and Physics, Paris-Orsay, France.

Louppe, G. (2019). Intelligence artificielle. Paper presented at Cycle de conférences "De l'homme préhistorique à l'homme robotique", Liège, Belgium.

Louppe, G. (2019). Doc'Café: "Siffler en travaillant? de Marx à l'intelligence artificielle". Paper presented at Doc'café, Liège, Belgium.

Louppe, G. (14 January 2019). Lectures on Deep Learning. Paper presented at GGI lectures on the theory of fundamental interactions, Firenze, Italy.

Louppe, G. (09 January 2019). Parameter inference and data modelling with deep learning. Paper presented at Flexible operation and advanced control workshop, Cambridge, United Kingdom.

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.

Louppe, G. (19 November 2018). Likelihood-free inference for Physical Sciences. Paper presented at Data Science Seminar Series, Grenoble, France.

Louppe, G. (16 November 2018). Likelihood-free inference in Physical Sciences. Paper presented at IIHE invited seminar, Brussels, Belgium.

Louppe, G. (26 October 2018). Likelihood-free inference, effectively. Paper presented at 1st Terascale School of Machine Learning, Hamburg, Germany.

Louppe, G. (17 October 2018). Constraining Effective Field Theories with Machine Learning. Paper presented at 3rd ATLAS Machine Learning Workshop, Geneva, Switzerland.

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

Hasib, A., Schaarschmidt, J., Gadatsch, S., Golling, T., Salamani, D., Ghosh, A., Rousseau, D., Cranmer, K., Stewart, G., & Louppe, G. (05 July 2018). New approaches using machine learning for fast shower simulation in ATLAS. Paper presented at ICHEP 2018 Seoul, Seoul, South Korea.

Louppe, G. (22 June 2018). Likelihood-free inference, effectively. Paper presented at AMVA4NewPhysics workshop, Athens, Greece.

Raimondo, F., Engemann, D., King, J.-R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez Slezak, D., Laureys, S., Naccache, L., Dehaene, S., & Sitt, J. (June 2018). Automated Machine Learning-based diagnosis of impaired consciousness: cross-center and protocol generalization of EEG biomarkers. Poster session presented at HBP International Conference: Understanding Consciousness, Barcelona, Spain.

Louppe, G. (2018). Deep Learning: Past, present and future. Paper presented at Journée d'étude de la SRBE «L’intelligence artificielle au sein du système électrique: Ses applications, potentialités et dangers», Brussels, Belgium.

Hermans, J., & Louppe, G. (2018). Gradient Energy Matching for Distributed Asynchronous Gradient Descent. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/226232.

Louppe, G. (2018). Deep Learning: Past, present and future. Paper presented at Lunch and Learn at NRB, Liège, Belgium.

Louppe, G. (18 January 2018). Adversarial Games for Particle Physics. Paper presented at Accelerating the Search for Dark Matter with Machine Learning, Leiden, Netherlands.

Engemann, D. A.* , Raimondo, F.* , King, J.-R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez-Slezak, D., Laureys, S., Naccache, L., Dehaene, S., & Sitt, J. D. (2018). Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain: a Journal of Neurology, 141 (11), 3179-3192. doi:10.1093/brain/awy251

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.

Henrion, I., Brehmer, J., Bruna, J., Cho, K., Cranmer, K., Louppe, G., & Rochette, G. (08 December 2017). Neural Message Passing for Jet Physics. Paper presented at Deep Learning for Physical Sciences workshop, NIPS 2018, Los Angeles, United States.

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.

Louppe, G. (08 December 2017). Adversarial Games for Particle Physics. Paper presented at Deep Learning for Physical Sciences workshop, NIPS 2018, Los Angeles, United States.

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.

Louppe, G. (02 November 2017). Teaching Machines to Discover Particles. Paper presented at HEP seminar, Nijmegen, Netherlands.

Louppe, G. (29 September 2017). Teaching Machines to Discover Particles. Paper presented at Nikhef Colloquium, Amsterdam, Netherlands.

Raimondo, F., Engemann, D., King, J.-R., Rohaut, B., Louppe, G., Laureys, S., Fernandez Slezak, D., Naccache, L., Dehaene, S., & Sitt, J. (August 2017). Automated Machine Learning-based diagnosis of impaired consciousness: cross-center and protocol generalization of EEG biomarkers. Paper presented at Annual congress of the International NeuroInformatics Coordinating Facility (INCF), Kuala Lumpur, Malaysia.

Louppe, G. (20 July 2017). Teaching Machines to Discover Particles. Paper presented at Hammers and Nails - Machine Learning and HEP, Rehovot, Israel.

Louppe, G., & Cranmer, K. (20 July 2017). Adversarial Variational Optimization of Non-Differentiable Simulators. Paper presented at Hammers and Nails - Machine Learning and HEP, Rehovot, Israel.

Louppe, G., Cho, K., Becot, C., & Cranmer, K. (07 June 2017). QCD-Aware Recursive Neural Networks for Jet Physics. Paper presented at ATLAS Machine Learning workshop, Geneva, Switzerland.

Louppe, G., Kagan, M., & Cranmer, K. (10 May 2017). Learning to pivot with adversarial networks. Paper presented at Data Science @ HEP 2017, Chicago, United States.

Louppe, G. (2017). Bayesian Optimisation with Scikit-Optimize. Paper presented at PyData Amsterdam, Amsterdam, Netherlands.

Louppe, G., Kagan, M., & Cranmer, K. (07 April 2017). Learning to pivot with adversarial networks. Paper presented at IIHE seminar, Brussels, Belgium.

Louppe, G. (21 March 2017). An introduction to Machine Learning with Scikit-Learn. Paper presented at IML Machine Learning Workshop, Geneva, Switzerland.

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. (17 November 2016). Learning to pivot with adversarial networks. Paper presented at ATLAS ML Forum, Geneva, Switzerland.

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

Louppe, G. (October 2016). Lectures on Machine Learning. Paper presented at Machine Learning and Data Science in Physics, Barcelona, Spain.

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.

Louppe, G. (03 August 2016). Learning to generate with adversarial networks. Paper presented at US ATLAS Physics Support, Software and Computing meeting, Chicago, United States.

Louppe, G. (21 July 2016). Learning to generate with adversarial networks. Paper presented at ATLAS ML Forum, Geneva, Switzerland.

Louppe, G. (07 July 2016). Learning to generate with adversarial networks. Paper presented at DS@HEP at the Simons Foundation, New York, United States.

Louppe, G. (27 June 2016). Learning to generate with adversarial networks. Paper presented at Software R&D: Next Gen Simulation, Geneva, Switzerland.

Louppe, G., Cranmer, K., & Pavez, J. (22 June 2016). Approximating likelihood ratios with Calibrated Classifiers. Paper presented at Second Machine Learning in High Energy Physics Summer School 2016, Lund, Sweden.

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.

Louppe, G. (2016). Robust and Calibrated Classifiers with Scikit-Learn. Paper presented at Zurich ML meetup, Zurich, Switzerland.

Louppe, G. (11 April 2016). Bayesian Optimisation. Paper presented at DIANA meeting, Geneva, Switzerland.

Louppe, G. (30 March 2016). Bayesian Optimisation. Paper presented at ATLAS ML workshop, Geneva, Switzerland.

Louppe, G., Cranmer, K., & Pavez, J. (29 March 2016). Approximating likelihood ratios with Calibrated Classifiers. Paper presented at ATLAS Machine Learning workshop, Geneva, Switzerland.

Louppe, G., Cranmer, K., & Pavez, J. (13 March 2016). Approximating likelihood ratios with Calibrated Classifiers. Paper presented at ETH Machine Learning seminar, Zurich, Switzerland.

Louppe, G. (18 February 2016). An introduction to Machine Learning with Scikit-Learn. Paper presented at Heavy Flavour Data Mining workshop, Zurich, Switzerland.

Louppe, G., & Head, T. (18 February 2016). Pitfalls of evaluating a classifier’s performance in high energy physics applications. Paper presented at Heavy Flavour Data Mining workshop, Zurich, Switzerland.

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., & Head, T. (11 December 2015). Pitfalls of evaluating a classifier’s performance in high energy physics applications. Paper presented at ALEPH workshop, NIPS 2015, Montréal, Canada.

Louppe, G. (12 November 2015). An introduction to Machine Learning with Scikit-Learn. Paper presented at Data Science at LHC, Geneva, Switzerland.

Louppe, G., & Head, T. (05 October 2015). Classification with a control channel: Don't cheat yourself. Paper presented at LHCb PPTS meeting, Geneva, Switzerland.

Louppe, G. (30 September 2015). Scikit-Learn tutorial. Paper presented at AstroHack week 2015, New York, United States.

Louppe, G. (21 September 2015). Understanding Random Forests. Paper presented at Software and Computing R&D Working Meeting, Geneva, Switzerland.

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. (23 April 2015). An introduction to Machine Learning with Scikit-Learn. Paper presented at LHCb Scikit-Learn tutorial, Geneva, Switzerland.

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

Louppe, G. (2015). Machine Learning for Author Disambiguation. Paper presented at INSPIRE@CERN Team Meeting, Geneva, Switzerland.

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.

Marée, R., Stevens, B., Rollus, L., Louppe, G., & Wehenkel, L. (October 2012). A rich internet application for remote visualization, collaborative annotation, and automated analysis of large-scale biomages. Poster session presented at Turning Images to Knowledge: Large-Scale 3D Image Annotation, Management, and Visualization, Ashburn, United States.

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., & Geurts, P. (11 December 2010). A zealous parallel gradient descent algorithm. Poster session presented at NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds, Whistler, Canada.

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.