| Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges B Bischl, M Binder, M Lang, T Pielok, J Richter, S Coors, J Thomas, ... Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13 (2 …, 2023 | 421 | 2023 |
| 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 | 314 | 2019 |
| Amlb: an automl benchmark P Gijsbers, MLP Bueno, S Coors, E LeDell, S Poirier, J Thomas, B Bischl, ... Journal of Machine Learning Research 25 (101), 1-65, 2024 | 58 | 2024 |
| Machine learning for the educational sciences S Hilbert, S Coors, E Kraus, B Bischl, A Lindl, M Frei, J Wild, S Krauss, ... Review of Education 9 (3), e3310, 2021 | 44 | 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, ... | 36 | 2019 |
| Automatic gradient boosting J Thomas, S Coors, B Bischl arXiv preprint arXiv:1807.03873, 2018 | 35 | 2018 |
| 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 | 24 | 2022 |
| 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 | 21 | 2023 |
| Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019 | 18 | 2019 |
| Predicting instructed simulation and dissimulation when screening for depressive symptoms S Goerigk, S Hilbert, A Jobst, P Falkai, M Bühner, C Stachl, B Bischl, ... European Archives of Psychiatry and Clinical Neuroscience 270, 153-168, 2020 | 10 | 2020 |
| Hyperparameter optimization: Foundations, algorithms, best practices and open challenges. arXiv B Bischl, M Binder, M Lang, T Pielok, J Richter, S Coors, J Thomas, ... arXiv preprint arXiv:2107.05847, 2021 | 9 | 2021 |
| A comprehensive machine learning benchmark study for radiomics-based survival analysis of CT imaging data in patients with hepatic metastases of CRC AT Stüber, S Coors, B Schachtner, T Weber, D Rügamer, A Bender, ... Investigative radiology 58 (12), 874-881, 2023 | 7 | 2023 |
| mlr3: Machine learning in R—Next generation M Lang, B Bischl, J Richter, P Schratz, G Casalicchio, S Coors, Q Au, ... | 7 | 2020 |
| mlr3learners: recommended learners for'mlr3'. R package version 0.4. 3 M Lang, Q Au, S Coors, P Schratz | 6 | 2020 |
| & Stachl, C.(2021) S Hilbert, S Coors, E Kraus, B Bischl, A Lindl, M Frei Machine learning for the educational sciences. Review of Education 9 (3), e3310, 0 | 5 | |
| Automatic Componentwise Boosting: An Interpretable AutoML System S Coors, D Schalk, B Bischl, D Rügamer arXiv preprint arXiv:2109.05583, 2021 | 3 | 2021 |
| Automatic gradient boosting S Coors | 3 | 2018 |
| Machine learning for spelling acquisition: How accurate is the prediction of specific spelling errors in German primary school students? R Böhme, S Coors, P Oster, M Munser-Kiefer, S Hilbert Computers and Education: Artificial Intelligence 6, 100233, 2024 | 1 | 2024 |
| Evolutionary Learning and Optimization J Renzullo, W Weimer, S Forrest, D Yazdani, MN Omidvar, AH Gandomi, ... ACM Transactions on 3 (4), 2023 | | 2023 |
| Revitalize the Potential of Radiomics: Interpretation and Feature Stability in Medical Imaging Analyses through Groupwise Feature Importance. AT Stüber, S Coors, M Ingrisch xAI (Late-breaking Work, Demos, Doctoral Consortium), 140-145, 2023 | | 2023 |
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichBestätigte E-Mail-Adresse bei stat.uni-muenchen.de
