| On Ranking and Generalization Bounds. W Rejchel Journal of Machine Learning Research 13 (5), 2012 | 72 | 2012 |
| Improving Lasso for model selection and prediction P Pokarowski, W Rejchel, A Sołtys, M Frej, J Mielniczuk Scandinavian Journal of Statistics 49 (2), 831-863, 2022 | 11 | 2022 |
| Sparse estimation in ising model via penalized Monte Carlo methods B Miasojedow, W Rejchel Journal of Machine Learning Research 19 (75), 1-26, 2018 | 11 | 2018 |
| Model selection consistency of U-statistics with convex loss and weighted lasso penalty W Rejchel Journal of Nonparametric Statistics 29 (4), 768-791, 2017 | 9 | 2017 |
| Rank-based Lasso-efficient methods for high-dimensional robust model selection W Rejchel, M Bogdan Journal of Machine Learning Research 21 (244), 1-47, 2020 | 8 | 2020 |
| Prediction and variable selection in high-dimensional misspecified binary classification K Furmańczyk, W Rejchel Entropy 22 (5), 543, 2020 | 7 | 2020 |
| Rank correlation estimators and their limiting distributions W Niemiro, W Rejchel Statistical Papers 50, 887-893, 2009 | 7 | 2009 |
| Oracle inequalities for ranking and U-processes with Lasso penalty W Rejchel Neurocomputing 239, 214-222, 2017 | 6 | 2017 |
| Lasso with convex loss: Model selection consistency and estimation W Rejchel Communications in Statistics-Theory and Methods 45 (7), 1989-2004, 2016 | 5 | 2016 |
| Fast rates for ranking with large families W Rejchel Neurocomputing 168, 1104-1110, 2015 | 5 | 2015 |
| Joint estimation of posterior probability and propensity score function for positive and unlabelled data K Furmańczyk, J Mielniczuk, W Rejchel, P Teisseyre arXiv preprint arXiv:2209.07787, 2022 | 4 | 2022 |
| Double logistic regression approach to biased positive-unlabeled data K Furmańczyk, J Mielniczuk, W Rejchel, P Teisseyre ECAI 2023, 764-771, 2023 | 3 | 2023 |
| Ranking-convex risk minimization W Rejchel International Journal of Computer and Information Engineering 3 (8), 1949-1955, 2009 | 3 | 2009 |
| Improving group Lasso for high-dimensional categorical data S Nowakowski, P Pokarowski, W Rejchel, A Sołtys International Conference on Computational Science, 455-470, 2023 | 2 | 2023 |
| Group Lasso merger for sparse prediction with high-dimensional categorical data S Nowakowski, P Pokarowski, W Rejchel arXiv preprint arXiv:2112.11114, 2021 | 2 | 2021 |
| Adaptive Monte Carlo Maximum Likelihood B Miasojedow, W Niemiro, J Palczewski, W Rejchel Challenges in Computational Statistics and Data Mining, 247-270, 2016 | 2 | 2016 |
| Asymptotics of Monte Carlo maximum likelihood estimators B Miasojedow, W Niemiro, J Palczewski, W Rejchel arXiv preprint arXiv:1412.6371, 2014 | 2 | 2014 |
| High-dimensional linear model selection motivated by multiple testing K Furmańczyk, W Rejchel Statistics 54 (1), 152-166, 2020 | 1 | 2020 |
| Evaluation of accuracy of digital map data via multiple comparisons A Doskocz, W Rejchel Bulletin of the Polish Academy of Sciences: Technical Sciences, 2016 | 1 | 2016 |
| Estimation under inequality constraints in univariate and multivariate linear models K Filipiak, D von Rosen, M Singull, W Rejchel | | 2024 |
