| 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 | 388 | 2023 |
| An Open Source AutoML Benchmark P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren ICML AutoML Workshop, 2019 | 298 | 2019 |
| mlrMBO: A modular framework for model-based optimization of expensive black-box functions B Bischl, J Richter, J Bossek, D Horn, J Thomas, M Lang arXiv preprint arXiv:1703.03373, 2017 | 205 | 2017 |
| 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 | 118* | 2022 |
| Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates J Thomas, A Mayr, B Bischl, M Schmid, A Smith, B Hofner Statistics and Computing 28, 673-687, 2018 | 83 | 2018 |
| 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 | 55 | 2020 |
| 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 | 53 | 2024 |
| Automatic Gradient Boosting J Thomas, S Coors, B Bischl ICML AutoML Workshop, 2018 | 35 | 2018 |
| Probing for sparse and fast variable selection with model-based boosting J Thomas, T Hepp, A Mayr, B Bischl Computational and Mathematical Methods in Medicine 2017, 8 pages, 2017 | 34 | 2017 |
| 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 | 23 | 2022 |
| Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning J Goschenhofer, FMJ Pfister, KA Yuksel, B Bischl, U Fietzek, J Thomas Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 22 | 2019 |
| Fusionkit: a generic toolkit for skeleton, marker and rigid-body tracking M Rietzler, F Geiselhart, J Thomas, E Rukzio Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive …, 2016 | 19 | 2016 |
| Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019 | 18 | 2019 |
| Deep semi-supervised learning for time-series classification J Goschenhofer Deep Learning Applications, Volume 4, 361-384, 2022 | 17 | 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 | 16 | 2023 |
| RAMBO: Resource-aware model-based optimization with scheduling for heterogeneous runtimes and a comparison with asynchronous model-based optimization H Kotthaus, J Richter, A Lang, J Thomas, B Bischl, P Marwedel, ... Learning and Intelligent Optimization: 11th International Conference, LION …, 2017 | 16 | 2017 |
| Automatic exploration of machine learning experiments on openml D Kühn, P Probst, J Thomas, B Bischl arXiv preprint arXiv:1806.10961, 2018 | 15 | 2018 |
| Towards human centered AutoML F Pfisterer, J Thomas, B Bischl arXiv preprint arXiv:1911.02391, 2019 | 13 | 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 | 12 | 2018 |
| mlr Tutorial J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ... arXiv preprint arXiv:1609.06146, 2016 | 9 | 2016 |
Bernd BischlChair of Statistical Learning and Data Science, LMU Munich在 stat.uni-muenchen.de 的电子邮件经过验证
