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
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, e1484, 2021
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
Automatic Gradient Boosting
J Thomas, S Coors, B Bischl
ICML AutoML Workshop, 2018
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
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
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, ...
Learning and Intelligent Optimization: 11th International Conference, LION …, 2017
Multi-objective automatic machine learning with autoxgboostmc
F Pfisterer, S Coors, J Thomas, B Bischl
arXiv preprint arXiv:1908.10796, 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
Deep Semi-supervised Learning for Time-Series Classification
J Goschenhofer
Deep Learning Applications, Volume 4, 361-384, 2022
mlr Tutorial
J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ...
arXiv preprint arXiv:1609.06146, 2016
Towards human centered AutoML
F Pfisterer, J Thomas, B Bischl
arXiv preprint arXiv:1911.02391, 2019
Lang, M. mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. arXiv 2017
B Bischl, J Richter, J Bossek, D Horn, J Thomas
arXiv preprint arXiv:1703.03373, 0
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
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
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