FusionKit: a generic toolkit for skeleton, marker and rigid-body tracking. - Michael Rietzler; Florian Geiselhart; Janek Thomas; Enrico Rukzio (EICS, 2016)

mlr Tutorial. - Julia Schiffner; Bernd Bischl; Michel Lang; Jakob Richter; Zachary M. Jones; Philipp Probst; Florian Pfisterer; Mason Gallo; Dominik Kirchhoff; Tobias Kühn; Janek Thomas; Lars Kotthoff (CoRR, 2016)

RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. - Helena Kotthaus; Jakob Richter; Andreas Lang 002; Janek Thomas; Bernd Bischl; Peter Marwedel; Jörg Rahnenführer; Michel Lang (LION, 2017)

Probing for Sparse and Fast Variable Selection with Model-Based Boosting. - Janek Thomas; Tobias Hepp 001; Andreas Mayr; Bernd Bischl (Comput. Math. Methods Medicine, 2017)

Corrigendum to "Probing for Sparse and Fast Variable Selection with Model-Based Boosting". - Janek Thomas; Tobias Hepp 001; Andreas Mayr; Bernd Bischl (Comput. Math. Methods Medicine, 2018)

Automatic Exploration of Machine Learning Experiments on OpenML. - Daniel Kühn; Philipp Probst; Janek Thomas; Bernd Bischl (CoRR, 2018)

Automatic Gradient Boosting. - Janek Thomas; Stefan Coors; Bernd Bischl (CoRR, 2018)

compboost: Modular Framework for Component-Wise Boosting. - Daniel Schalk; Janek Thomas; Bernd Bischl (J. Open Source Softw., 2018)

Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. - Janek Thomas; Andreas Mayr; Bernd Bischl; Matthias Schmid; Adam Smith; Benjamin Hofner (Stat. Comput., 2018)

compboost. - Daniel Schalk; Janek Thomas; Bernd Bischl (Zenodo, 2018)

Wearable-Based Parkinson's Disease Severity Monitoring Using Deep Learning. - Jann Goschenhofer; Franz Michael Josef Pfister; Kamer Ali Yuksel; Bernd Bischl; Urban Fietzek; Janek Thomas (ECML/PKDD (3), 2019)

An Open Source AutoML Benchmark. - Pieter Gijsbers; Erin LeDell; Janek Thomas; Sébastien Poirier; Bernd Bischl; Joaquin Vanschoren (CoRR, 2019)

Multi-Objective Automatic Machine Learning with AutoxgboostMC. - Florian Pfisterer; Stefan Coors; Janek Thomas; Bernd Bischl (CoRR, 2019)

Towards Human Centered AutoML. - Florian Pfisterer; Janek Thomas; Bernd Bischl (CoRR, 2019)

Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection. - Martin Binder; Julia Moosbauer; Janek Thomas; Bernd Bischl (CoRR, 2019)

Multi-objective hyperparameter tuning and feature selection using filter ensembles. - Martin Binder; Julia Moosbauer; Janek Thomas; Bernd Bischl (GECCO, 2020)

Deep Semi-supervised Learning for Time Series Classification. - Jann Goschenhofer; Rasmus Hvingelby; David Rügamer; Janek Thomas; Moritz Wagner 001; Bernd Bischl (ICMLA, 2021)

Automated Online Experiment-Driven Adaptation-Mechanics and Cost Aspects. - Ilias Gerostathopoulos; Frantisek Plásil; Christian Prehofer; Janek Thomas; Bernd Bischl (IEEE Access, 2021)

Tackling Neural Architecture Search With Quality Diversity Optimization. - Lennart Schneider; Florian Pfisterer; Paul Kent; Jürgen Branke; Bernd Bischl; Janek Thomas (AutoML, 2022)

A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. - Lennart Schneider; Florian Pfisterer; Janek Thomas; Bernd Bischl (GECCO Companion, 2022)

Multi-Objective Hyperparameter Optimization - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (CoRR, 2022)

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. - Florian Pargent; Florian Pfisterer; Janek Thomas; Bernd Bischl (Comput. Stat., 2022)

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. - Lennart Schneider; Bernd Bischl; Janek Thomas (GECCO, 2023)

MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization. - Noor H. Awad; Ayushi Sharma; Philipp Muller; Janek Thomas; Frank Hutter (CoRR, 2023)

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. - Bernd Bischl; Martin Binder; Michel Lang; Tobias Pielok; Jakob Richter; Stefan Coors; Janek Thomas; Theresa Ullmann; Marc Becker; Anne-Laure Boulesteix; Difan Deng; Marius Lindauer (WIREs Data. Mining. Knowl. Discov., 2023)

Structured Verification of Machine Learning Models in Industrial Settings. - Sai Rahul Kaminwar; Jann Goschenhofer; Janek Thomas; Ingo Thon; Bernd Bischl (Big Data, 2023)

Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (ACM Trans. Evol. Learn. Optim., 2023)

AMLB: an AutoML Benchmark. - Pieter Gijsbers; Marcos L. P. Bueno; Stefan Coors; Erin LeDell; Sébastien Poirier; Janek Thomas; Bernd Bischl; Joaquin Vanschoren (J. Mach. Learn. Res., 2024)