| Explaining hyperparameter optimization via partial dependence plots J Moosbauer, J Herbinger, G Casalicchio, M Lindauer, B Bischl Advances in Neural Information Processing Systems 34, 2280-2291, 2021 | 70 | 2021 |
| Multi-objective hyperparameter tuning and feature selection using filter ensembles M Binder, J Moosbauer, J Thomas, B Bischl Proceedings of the 2020 genetic and evolutionary computation conference, 471-479, 2020 | 63 | 2020 |
| 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 |
| 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 | 29 | 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 | 28 | 2022 |
| Longitudinal assessment of multiple sclerosis lesion load with synthetic magnetic resonance imaging—a multicenter validation study S Schlaeger, HB Li, T Baum, C Zimmer, J Moosbauer, S Byas, M Mühlau, ... Investigative Radiology 58 (5), 320-326, 2023 | 9 | 2023 |
| 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 | 9 | 2022 |
| Improving accuracy of interpretability measures in hyperparameter optimization via Bayesian algorithm execution J Moosbauer, G Casalicchio, M Lindauer, B Bischl arXiv preprint arXiv:2206.05447, 2022 | 9 | 2022 |
| Faster and better: how anomaly detection can accelerate and improve reporting of head computed tomography T Finck, J Moosbauer, M Probst, S Schlaeger, M Schuberth, D Schinz, ... Diagnostics 12 (2), 452, 2022 | 9 | 2022 |
| Towards explaining hyperparameter optimization via partial dependence plots J Moosbauer, J Herbinger, G Casalicchio, M Lindauer, B Bischl 8th ICML Workshop on Automated Machine Learning (AutoML), 2020 | 9 | 2020 |
| Automated pathology detection and patient triage in routinely acquired head computed tomography scans T Finck, D Schinz, L Grundl, R Eisawy, M Yigitsoy, J Moosbauer, F Pfister, ... Investigative Radiology 56 (9), 571-578, 2021 | 6 | 2021 |
| Automated Detection of Ischemic Stroke and Subsequent Patient Triage in Routinely Acquired Head CT T Finck, D Schinz, L Grundl, R Eisawy, M Yiğitsoy, J Moosbauer, ... Clinical Neuroradiology 32 (2), 419-426, 2022 | 2 | 2022 |
| Position: A Call to Action for a Human-Centered AutoML Paradigm M Lindauer, F Karl, A Klier, J Moosbauer, A Tornede, A Mueller, F Hutter, ... arXiv preprint arXiv:2406.03348, 2024 | 1 | 2024 |
| A platform for deep learning on (meta) genomic sequences P Münch, R Mreches, XY To, HA Gündüz, J Moosbauer, S Klawitter, ... | 1 | 2023 |
| Optimized model architectures for deep learning on genomic data HA Gündüz, R Mreches, J Moosbauer, G Robertson, XY To, EA Franzosa, ... Communications Biology 7 (1), 516, 2024 | | 2024 |
| Author Correction: Optimized model architectures for deep learning on genomic data HA Gündüz, R Mreches, J Moosbauer, G Robertson, XY To, EA Franzosa, ... Communications Biology 7, 2024 | | 2024 |
| Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis CA Scholbeck, J Moosbauer, G Casalicchio, H Gupta, B Bischl, ... arXiv preprint arXiv:2312.13234, 2023 | | 2023 |
| Towards explainable automated machine learning J Moosbauer lmu, 2023 | | 2023 |
| Optimized model architectures for deep learning on genomic data P Münch, HA Gündüz, R Mreches, J Moosbauer, G Robertson, XY To, ... | | 2023 |
| Evolutionary Learning and Optimization J Renzullo, W Weimer, S Forrest, D Yazdani, MN Omidvar, AH Gandomi, ... ACM Transactions on 3 (4), 2023 | | 2023 |
