Janek Thomas

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	All	Since 2016
Citations	252	249
h-index	6	6
i10-index	6	6

120

2017201820192020202113 40 68 109 15

Co-authors

Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Andreas MayrDept. of Medical Biometry, Informatics and Epidemiology, University of BonnVerified email at uni-bonn.de
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Benjamin HofnerHead of Section Biostatistics, Paul-Ehrlich-InstituteVerified email at pei.de
Jakob BossekWestfälische Wilhelms Universität MünsterVerified email at wi.uni-muenster.de
Daniel HornStatistical Methods for Big Data, TU DortmundVerified email at tu-dortmund.de
Adam D. SmithQuantitative Ecologist; US Fish & Wildlife ServiceVerified email at fws.gov
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Tobias HeppDepartment of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-NürnbergVerified email at fau.de
Pieter GijsbersPhD Student, Eindhoven University of TechnologyVerified email at tue.nl
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Franz MJ PfisterData Science, LMUVerified email at campus.lmu.de
Enrico RukzioUlm UniversityVerified email at uni-ulm.de
Michael RietzlerUlm UniversityVerified email at uni-ulm.de
Florian GeiselhartResearch Associate, Institute of Media Informatics, Ulm UniversityVerified email at uni-ulm.de
Jann GoschenhoferPhD Candidate, LMUVerified email at stat.uni-muenchen.de
Kamer Ali YUKSELChief Data Scientist @ ConnectedLife GmbHVerified email at badehotels.com
Julia MoosbauerPhD Student, Ludwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de

Janek Thomas

Fraunhofer Institute for Integrated Circuits IIS

Verified email at iis.fraunhofer.de - Homepage

Artificial Intelligence Machine Learning AutoML Statistics Optimization


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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	97	2017
An Open Source AutoML Benchmark P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren ICML AutoML Workshop, 2019	56	2019
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	32	2018
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	15	2016
Automatic Gradient Boosting J Thomas, S Coors, B Bischl ICML AutoML Workshop, 2018	10	2018
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	10	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
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	5	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	5	2018
Automatic Exploration of Machine Learning Experiments on OpenML D Kühn, P Probst, J Thomas, B Bischl arXiv preprint arXiv:1806.10961, 2018	5	2018
Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection M Binder, J Moosbauer, J Thomas, B Bischl arXiv preprint arXiv:1912.12912, 2019	3*	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
Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019	2	2019
A benchmark experiment on how to encode categorical features in predictive modeling F Pargent, B Bischl, J Thomas M. Sc. Thesis, Ludwig-Maximilians–Universitat Munchen, pp12, 2019	2	2019
compboost: Modular Framework for Component-Wise Boosting D Schalk, J Thomas, B Bischl Journal of Open Source Software 3 (30), 967, 2018	1	2018
Deep Semi-Supervised Learning for Time Series Classification J Goschenhofer, R Hvingelby, D Rügamer, J Thomas, M Wagner, B Bischl arXiv preprint arXiv:2102.03622, 2021		2021
Towards Human Centered AutoML F Pfisterer, J Thomas, B Bischl arXiv preprint arXiv:1911.02391, 2019		2019
Recognition of subjects with early-stage Parkinson from free-living unilateral wrist-sensor data using a hierarchical machine learning model FMJ Pfister, A von Schumann, J Bemetz, J Thomas, A Ceballos-Baumann, ... JOURNAL OF NEURAL TRANSMISSION 126 (5), 663-663, 2019		2019
Gradient boosting in automatic machine learning: feature selection and hyperparameter optimization J Thomas Ludwig-Maximilians-University Munich, 2019		2019
Boosting algorithms for GAMLSS B Hofner, A Mayr, N Fenske, J Thomas, M Schmid

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