| On the use of random forest for two-sample testing S Hediger, L Michel, J Näf Computational Statistics & Data Analysis 170, 107435, 2022 | 47 | 2022 |
| Distributional random forests: Heterogeneity adjustment and multivariate distributional regression D Cevid, L Michel, J Näf, P Bühlmann, N Meinshausen Journal of Machine Learning Research 23 (333), 1-79, 2022 | 43 | 2022 |
| quantregForest: Quantile Regression Forests, 2007 N Meinshausen, L Michel R package version 0.2-2, 0 | 20 | |
| Solving optimal stopping problems with Deep Q-Learning J Ery, L Michel arXiv preprint arXiv:2101.09682, 2021 | 8 | 2021 |
| Novel approach to visualize the inter-dependencies between maternal sensitization, breast milk immune components and human milk oligosaccharides in the LIFE Child cohort L Michel, M Shevlyakova, E Ní Cléirigh, E Eckhardt, S Holvoet, S Nutten, ... Plos one 15 (4), e0230472, 2020 | 7 | 2020 |
| PKLM: A flexible MCAR test using Classification ML Spohn, J Näf, L Michel, N Meinshausen arXiv preprint arXiv:2109.10150, 2021 | 6 | 2021 |
| Imputation scores J Näf, ML Spohn, L Michel, N Meinshausen The Annals of Applied Statistics 17 (3), 2452-2472, 2023 | 5 | 2023 |
| Proper scoring rules for missing value imputation L Michel, J Näf, ML Spohn, N Meinshausen arXiv e-prints, arXiv: 2106.03742, 2021 | 4 | 2021 |
| High probability lower bounds for the total variation distance L Michel, J Näf, N Meinshausen arXiv preprint arXiv:2005.06006, 2020 | 2 | 2020 |
| Distributional Metrics In Computational Statistics L Michel ETH Zurich, 2021 | | 2021 |
Nicolai MeinshausenProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
