Jakob Richter
Jakob Richter
Ph.D. Candidate, TU Dortmund
Verified email at statistik.tu-dortmund.de - Homepage
Title
Cited by
Cited by
Year
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
4482016
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
862017
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
262019
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
142018
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
102019
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
102016
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
62017
BBmisc: Miscellaneous Helper Functions for B
B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann
Bischl. R package version 1, 2017
62017
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
62016
Model-based optimization of subgroup weights for survival analysis
J Richter, K Madjar, J Rahnenführer
Bioinformatics 35 (14), i484-i491, 2019
52019
Machine Learning in R
M Lang, J Richter
42019
mlr Tutorial
J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ...
arXiv preprint arXiv:1609.06146, 2016
32016
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
12017
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
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