| Scikit-learn: Machine learning in Python F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... https://arxiv.org/abs/1201.0490, 2012 | 83619 | 2012 |
| API design for machine learning software: experiences from the scikit-learn project L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... arXiv:1309.0238, 2013 | 3043 | 2013 |
| Understanding variable importances in forests of randomized trees G Louppe, L Wehenkel, A Sutera, P Geurts Advances in neural information processing systems, 431-439, 2013 | 1219 | 2013 |
| Understanding Random Forests: From Theory to Practice G Louppe arXiv preprint arXiv:1407.7502, 2014 | 1091 | 2014 |
| The frontier of simulation-based inference K Cranmer, J Brehmer, G Louppe Proceedings of the National Academy of Sciences 117 (48), 30055-30062, 2020 | 656 | 2020 |
| Scikit-learn: Machine learning without learning the machinery G Varoquaux, L Buitinck, G Louppe, O Grisel, F Pedregosa, A Mueller GetMobile: Mobile Computing and Communications 19 (1), 29-33, 2015 | 537 | 2015 |
| Learning to Pivot with Adversarial Networks G Louppe, M Kagan, K Cranmer arXiv:1611.01046, 2016 | 276 | 2016 |
| Machine learning in high energy physics community white paper K Albertsson, P Altoe, D Anderson, M Andrews, JP Araque Espinosa, ... Journal of Physics: Conference Series 1085, 022008, 2018 | 261 | 2018 |
| Robust EEG-based cross-site and cross-protocol classification of states of consciousness DA Engemann, F Raimondo, JR King, B Rohaut, G Louppe, F Faugeras, ... Brain 141 (11), 3179-3192, 2018 | 242 | 2018 |
| QCD-Aware Recursive Neural Networks for Jet Physics G Louppe, K Cho, C Becot, K Cranmer arXiv:1702.00748, 2017 | 214 | 2017 |
| Approximating Likelihood Ratios with Calibrated Discriminative Classifiers K Cranmer, J Pavez, G Louppe arXiv:1506.02169, 2016 | 196 | 2016 |
| Ensembles on random patches G Louppe, P Geurts Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012 | 194 | 2012 |
| Constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical review letters 121 (11), 111801, 2018 | 181 | 2018 |
| Collaborative analysis of multi-gigapixel imaging data using Cytomine R Marée, L Rollus, B Stévens, R Hoyoux, G Louppe, R Vandaele, ... Bioinformatics 32 (9), 1395-1401, 2016 | 168 | 2016 |
| Unconstrained monotonic neural networks A Wehenkel, G Louppe Advances in neural information processing systems 32, 2019 | 161 | 2019 |
| A guide to constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical Review D 98 (5), 052004, 2018 | 160 | 2018 |
| Mining gold from implicit models to improve likelihood-free inference J Brehmer, G Louppe, J Pavez, K Cranmer Proceedings of the National Academy of Sciences 117 (10), 5242-5249, 2020 | 158 | 2020 |
| Likelihood-free MCMC with Amortized Approximate Ratio Estimators J Hermans, V Begy, G Louppe International Conference on Machine Learning, 4239-4248, 2020 | 149* | 2020 |
| scikit-optimize/scikit-optimize T Head, MechCoder, G Louppe, I Shcherbatyi Zenodo, 2018 | 146* | 2018 |
| Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning J Brehmer, S Mishra-Sharma, J Hermans, G Louppe, K Cranmer arXiv preprint arXiv:1909.02005, 2019 | 99 | 2019 |
Gaël VaroquauxResearch director, INRIAVerified email at normalesup.org
