| mlr3: A modern object-oriented machine learning framework in R M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ... Journal of Open Source Software 4 (44), 1903, 2019 | 169 | 2019 |
| Learning multiple defaults for machine learning algorithms F Pfisterer, JN van Rijn, P Probst, AC Müller, B Bischl Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 30 | 2021 |
| Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features F Pargent, F Pfisterer, J Thomas, B Bischl Computational Statistics 37 (5), 2671-2692, 2022 | 24 | 2022 |
| mlr3: A modern object-oriented machine learning framework in RJ Open Source Softw M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ... | 20 | 2019 |
| mlr3pipelines—Flexible machine learning pipelines in r M Binder, F Pfisterer, M Lang, L Schneider, L Kotthoff, B Bischl The Journal of Machine Learning Research 22 (1), 8314-8320, 2021 | 17 | 2021 |
| Yahpo gym-an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization F Pfisterer, L Schneider, J Moosbauer, M Binder, B Bischl International Conference on Automated Machine Learning, 3/1-39, 2022 | 15 | 2022 |
| Debiasing classifiers: is reality at variance with expectation? A Agrawal, F Pfisterer, B Bischl, F Buet-Golfouse, S Sood, J Chen, S Shah, ... arXiv preprint arXiv:2011.02407, 2020 | 15 | 2020 |
| Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019 | 12 | 2019 |
| Benchmarking time series classification--Functional data vs machine learning approaches F Pfisterer, L Beggel, X Sun, F Scheipl, B Bischl arXiv preprint arXiv:1911.07511, 2019 | 11 | 2019 |
| Meta learning for defaults: Symbolic defaults JN van Rijn, F Pfisterer, J Thomas, A Muller, B Bischl, J Vanschoren Neural Information Processing Workshop on Meta-Learning, 2018 | 10 | 2018 |
| Meta-learning for symbolic hyperparameter defaults P Gijsbers, F Pfisterer, JN van Rijn, B Bischl, J Vanschoren Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 9 | 2021 |
| High dimensional restrictive federated model selection with multi-objective bayesian optimization over shifted distributions X Sun, A Bommert, F Pfisterer, J Rähenfürher, M Lang, B Bischl Intelligent Systems and Applications: Proceedings of the 2019 Intelligent …, 2020 | 9 | 2020 |
| mlr Tutorial J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ... arXiv preprint arXiv:1609.06146, 2016 | 8 | 2016 |
| Deepregression: a flexible neural network framework for semi-structured deep distributional regression D Rügamer, C Kolb, C Fritz, F Pfisterer, P Kopper, B Bischl, R Shen, ... arXiv preprint arXiv:2104.02705, 2021 | 7 | 2021 |
| Towards human centered AutoML F Pfisterer, J Thomas, B Bischl arXiv preprint arXiv:1911.02391, 2019 | 7 | 2019 |
| Automated benchmark-driven design and explanation of hyperparameter optimizers J Moosbauer, M Binder, L Schneider, F Pfisterer, M Becker, M Lang, ... IEEE Transactions on Evolutionary Computation 26 (6), 1336-1350, 2022 | 6 | 2022 |
| mcboost: Multi-Calibration Boosting for R F Pfisterer, C Kern, S Dandl, M Sun, MP Kim, B Bischl Journal of Open Source Software 6 (64), 3453, 2021 | 6 | 2021 |
| Mutation is all you need L Schneider, F Pfisterer, M Binder, B Bischl arXiv preprint arXiv:2107.07343, 2021 | 6 | 2021 |
| mlr3book M Becker, M Binder, B Bischl, N Foss, L Kotthoff, M Lan, F Pfisterer, ... URl: https://mlr3book. mlr-org. com 28, 29-30, 2021 | 6 | 2021 |
| Collecting empirical data about hyperparameters for data driven AutoML M Binder, F Pfisterer, B Bischl Democratizing Machine Learning Contributions in AutoML and Fairness, 93, 2020 | 6 | 2020 |
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
