| Correlation and variable importance in random forests B Gregorutti, B Michel, P Saint-Pierre Statistics and Computing 27 (3), 659-678, 2017 | 1286 | 2017 |
| An introduction to topological data analysis: fundamental and practical aspects for data scientists F Chazal, B Michel Frontiers in artificial intelligence 4, 667963, 2021 | 1046 | 2021 |
| Slope heuristics: overview and implementation JP Baudry, C Maugis, B Michel Statistics and Computing 22 (2), 455-470, 2012 | 279 | 2012 |
| Grouped variable importance with random forests and application to multiple functional data analysis B Gregorutti, B Michel, P Saint-Pierre Computational Statistics & Data Analysis 90, 15-35, 2015 | 188 | 2015 |
| Robust topological inference: Distance to a measure and kernel distance F Chazal, B Fasy, F Lecci, B Michel, A Rinaldo, L Wasserman Journal of Machine Learning Research 18 (159), 1-40, 2018 | 182 | 2018 |
| Subsampling methods for persistent homology F Chazal, B Fasy, F Lecci, B Michel, A Rinaldo, L Wasserman International Conference on Machine Learning, 2143-2151, 2015 | 162 | 2015 |
| Convergence rates for persistence diagram estimation in topological data analysis F Chazal, M Glisse, C Labruère, B Michel International Conference on Machine Learning, 163-171, 2014 | 158* | 2014 |
| Estimating the reach of a manifold E Aamari, J Kim, F Chazal, B Michel, A Rinaldo, L Wasserman | 145 | 2019 |
| Statistical Analysis and Parameter Selection for Mapper M Carriere, B Michel, S Oudot Journal of Machine Learning Research 19 (12), 2018 | 122 | 2018 |
| A non asymptotic penalized criterion for Gaussian mixture model selection C Maugis, B Michel ESAIM: Probability and Statistics 15, 41-68, 2011 | 100 | 2011 |
| Data-driven penalty calibration: A case studyfor Gaussian mixture model selection C Maugis, B Michel ESAIM: Probability and Statistics 15, 320-339, 2011 | 57* | 2011 |
| Deconvolution for the Wasserstein metric and geometric inference C Caillerie, F Chazal, J Dedecker, B Michel International Conference on Geometric Science of Information, 561-568, 2013 | 56 | 2013 |
| Rates of convergence for robust geometric inference F Chazal, P Massart, B Michel Electron. J. Statist. 10 (2), 2243-2286, 2016 | 41 | 2016 |
| Adaptive density estimation for clustering with Gaussian mixtures C Maugis-Rabusseau, B Michel ESAIM: Probability and Statistics 17, 698-724, 2013 | 37 | 2013 |
| Oil production: A probabilistic model of the Hubbert curve B Michel Applied Stochastic Models in Business and Industry 27 (4), 434-449, 2011 | 33 | 2011 |
| Minimax rates of convergence for Wasserstein deconvolution with supersmooth errors in any dimension J Dedecker, B Michel Journal of Multivariate Analysis 122, 278-291, 2013 | 30 | 2013 |
| Learning with tree tensor networks: complexity estimates and model selection B Michel, A Nouy Bernoulli 28 (2), 910-936, 2022 | 24 | 2022 |
| Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches FB Azzouz, B Michel, H Lasla, W Gouraud, AF François, F Girka, ... Computers in Biology and Medicine 129, 104171, 2021 | 22 | 2021 |
| Improved rates for Wasserstein deconvolution with ordinary smooth error in dimension one J Dedecker, A Fischer, B Michel | 20 | 2015 |
| Model selection for simplicial approximation C Caillerie, B Michel Foundations of Computational Mathematics 11 (6), 707-731, 2011 | 18 | 2011 |
Frederic ChazalINRIA Saclay - Ile de FranceVerified email at inria.fr
