| 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 | 70 | 2017 |
| 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 (3), 673-687, 2018 | 28 | 2018 |
| An Open Source AutoML Benchmark P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren ICML AutoML Workshop, 2019 | 19 | 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 | 12 | 2016 |
| 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 | 8 | 2017 |
| Automatic Gradient Boosting J Thomas, S Coors, B Bischl ICML AutoML Workshop, 2018 | 6 | 2018 |
| 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, ... International Conference on Learning and Intelligent Optimization, 180-195, 2017 | 5 | 2017 |
| Automatic Exploration of Machine Learning Experiments on OpenML D Kühn, P Probst, J Thomas, B Bischl arXiv preprint arXiv:1806.10961, 2018 | 4 | 2018 |
| Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019 | 3 | 2019 |
| mlr Tutorial J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ... arXiv preprint arXiv:1609.06146, 2016 | 3 | 2016 |
| 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 | 2 | 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 | 2 | 2018 |
| Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection M Binder, J Moosbauer, J Thomas, B Bischl arXiv preprint arXiv:1912.12912, 2019 | | 2019 |
| Towards Human Centered AutoML F Pfisterer, J Thomas, B Bischl arXiv preprint arXiv:1911.02391, 2019 | | 2019 |
| Recognition of subjects with early-stage Parkinson from free-living unilateral wrist-sensor data using a hierarchical machine learning model FMJ Pfister, A von Schumann, J Bemetz, J Thomas, A Ceballos-Baumann, ... JOURNAL OF NEURAL TRANSMISSION 126 (5), 663-663, 2019 | | 2019 |
| Gradient boosting in automatic machine learning: feature selection and hyperparameter optimization J Thomas Ludwig-Maximilians-University Munich, 2019 | | 2019 |
| A Benchmark Experiment on How to Encode Categorical Features in Predictive Modeling F Pargent, B Bischl, J Thomas | | 2019 |
| compboost: Modular Framework for Component-Wise Boosting D Schalk, J Thomas, B Bischl Journal of Open Source Software 3 (30), 967, 2018 | | 2018 |
| Boosting algorithms for GAMLSS B Hofner, A Mayr, N Fenske, J Thomas, M Schmid | | |
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
