| mlr: Machine Learning in R B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ... The Journal of Machine Learning Research 17 (1), 5938-5942, 2016 | 468 | 2016 |
| 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 | 92 | 2017 |
| Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data P Schratz, J Muenchow, E Iturritxa, J Richter, A Brenning Ecological Modelling 406, 109-120, 2019 | 29 | 2019 |
| Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data P Schratz, J Muenchow, E Iturritxa, J Richter, A Brenning arXiv preprint arXiv:1803.11266, 2018 | 15 | 2018 |
| 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 | 12 | 2019 |
| Faster model-based optimization through resource-aware scheduling strategies J Richter, H Kotthaus, B Bischl, P Marwedel, J Rahnenführer, M Lang International Conference on Learning and Intelligent Optimization, 267-273, 2016 | 10 | 2016 |
| BBmisc: Miscellaneous Helper Functions for B B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann Bischl. R package version 1, 2017 | 7 | 2017 |
| 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 | 6 | 2017 |
| ParamHelpers: Helpers for parameters in black-box optimization, tuning, and machine learning B Bischl, M Lang, J Bossek, D Horn, K Schork, J Richter, P Kerschke R package version 1, 23, 2016 | 6 | 2016 |
| Model-based optimization of subgroup weights for survival analysis J Richter, K Madjar, J Rahnenführer Bioinformatics 35 (14), i484-i491, 2019 | 5 | 2019 |
| Machine Learning in R M Lang, J Richter | 4 | 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 |
| mlrHyperopt: Effortless and collaborative hyperparameter optimization experiments J Richter, J Rahnenführer, M Lang The R user conference, useR! 2017 July 4-7 2017, 78-, 2017 | 1 | 2017 |
| Model-based optimization with concept drifts J Richter, J Shi, JJ Chen, J Rahnenführer, M Lang Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 877-885, 2020 | | 2020 |
| Extending Model-Based Optimization with Resource-Aware Parallelization and for Dynamic Optimization Problems J Richter TU Dortmund, 2020 | | 2020 |
| Model-Based Optimization on Parallel Infrastructures and Functions with Concept Drift J Richter Technical report for Collaborative Research Center SFB 876 Providing …, 2018 | | 2018 |
| Selection of Optimal Subgroup Weights for Survival Analysis J Richter, K Madjar, J Rahnenführer Ulmer Informatik-Berichte, 32, 2018 | | 2018 |
| Faster Model Based Optimization through Resource Aware Scheduling and asynchronous Evaluations J Richter Technical report for Collaborative Research Center SFB 876 Providing …, 2016 | | 2016 |
| Faster Model Based Optimization through Resource Aware Scheduling J Richter Technical report for Collaborative Research Center SFB 876 Providing …, 2015 | | 2015 |
| Analysing geo data on a street level performed on an example of road accidents in London J Richter | | |
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
