| 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 | 335 | 2019 |
| 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 | 113 | 2022 |
| 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 | 44 | 2022 |
| 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 | 39 | 2021 |
| 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, ... | 39 | 2019 |
| mlr3pipelines-flexible machine learning pipelines in r M Binder, F Pfisterer, M Lang, L Schneider, L Kotthoff, B Bischl Journal of Machine Learning Research 22 (184), 1-7, 2021 | 29 | 2021 |
| Multi-objective hyperparameter optimization in machine learning—An overview F Karl, T Pielok, J Moosbauer, F Pfisterer, S Coors, M Binder, L Schneider, ... ACM Transactions on Evolutionary Learning and Optimization 3 (4), 1-50, 2023 | 28 | 2023 |
| Multi-Objective Hyperparameter Optimization--An Overview F Karl, T Pielok, J Moosbauer, F Pfisterer, S Coors, M Binder, L Schneider, ... arXiv preprint arXiv:2206.07438, 2022 | 27 | 2022 |
| Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019 | 21 | 2019 |
| 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 | 19 | 2020 |
| Can fairness be automated? Guidelines and opportunities for fairness-aware AutoML H Weerts, F Pfisterer, M Feurer, K Eggensperger, E Bergman, N Awad, ... Journal of Artificial Intelligence Research 79, 639-677, 2024 | 18 | 2024 |
| 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 | 18 | 2019 |
| 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 | 15 | 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 | 15 | 2020 |
| Towards human centered AutoML F Pfisterer, J Thomas, B Bischl arXiv preprint arXiv:1911.02391, 2019 | 14 | 2019 |
| mlr3 book M Becker, M Binder, B Bischl, N Foss, L Kotthoff, M Lan, F Pfisterer, ... URl: https://mlr3book. mlr-org. com 28, 29-30, 2021 | 13 | 2021 |
| 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 | 12 | 2021 |
| 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 | 12 | 2021 |
| 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 | 12 | 2018 |
| 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 | 10 | 2020 |
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichBestätigte E-Mail-Adresse bei stat.uni-muenchen.de
