Janek Thomas
Cited by
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An Open Source AutoML Benchmark
P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren
ICML AutoML Workshop, 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
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
Hyperparameter optimization: Foundations, algorithms, best practices and open challenges
B Bischl, M Binder, M Lang, T Pielok, J Richter, S Coors, J Thomas, ...
arXiv preprint arXiv:2107.05847, 2021
Automatic Gradient Boosting
J Thomas, S Coors, B Bischl
ICML AutoML Workshop, 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
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
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
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
F Pargent, F Pfisterer, J Thomas, B Bischl
Computational Statistics, 1-22, 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
Automatic exploration of machine learning experiments on openml
D Kühn, P Probst, J Thomas, B Bischl
arXiv preprint arXiv:1806.10961, 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
mlr Tutorial
J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ...
arXiv preprint arXiv:1609.06146, 2016
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
Deep semi-supervised learning for time series classification
J Goschenhofer, R Hvingelby, D Rügamer, J Thomas, M Wagner, B Bischl
2021 20th IEEE International Conference on Machine Learning and Applications …, 2021
Multi-objective automatic machine learning with autoxgboostmc
F Pfisterer, S Coors, J Thomas, B Bischl
arXiv preprint arXiv:1908.10796, 2019
Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects
I Gerostathopoulos, F Plášil, C Prehofer, J Thomas, B Bischl
IEEE Access 9, 58079-58087, 2021
Towards human centered AutoML
F Pfisterer, J Thomas, B Bischl
arXiv preprint arXiv:1911.02391, 2019
compboost: Modular Framework for Component-Wise Boosting
D Schalk, J Thomas, B Bischl
Journal of Open Source Software 3 (30), 967, 2018
Gradient boosting in automatic machine learning: feature selection and hyperparameter optimization
J Thomas
Ludwig-Maximilians-University Munich, 2019
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