Janek Thomas

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Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Andreas MayrDept. of Medical Biometry, Informatics and Epidemiology, University of BonnVerified email at uni-bonn.de
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Pieter GijsbersEindhoven University of TechnologyVerified email at tue.nl
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Jakob BossekRWTH Aachen UniversityVerified email at aim.rwth-aachen.de
Daniel HornStatistical Methods for Big Data, TU DortmundVerified email at tu-dortmund.de
Benjamin HofnerHead of Section Biostatistics, Paul-Ehrlich-InstituteVerified email at pei.de
Tobias HeppDepartment of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-NürnbergVerified email at fau.de
Adam D. SmithQuantitative Ecologist; US Fish & Wildlife ServiceVerified email at fws.gov
Julia MoosbauerPhD Student, Ludwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de
Jann GoschenhoferPhD Candidate, LMUVerified email at stat.uni-muenchen.de
Franz MJ PfisterData Science, LMUVerified email at campus.lmu.de
Florian PargentPsychological Methods and Assessment, LMU MunichVerified email at psy.lmu.de
Philipp ProbstVerified email at digital-ratio.de
Kamer Ali YUKSELSr. Principal AI Architect @ aiXplainVerified email at badehotels.com
Enrico RukzioUlm UniversityVerified email at uni-ulm.de

Janek Thomas

Ludwig-Maximilians-University Munich

Verified email at stat.uni-muenchen.de - Homepage

Artificial Intelligence Machine Learning AutoML Statistics Optimization


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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	197	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	169	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	58	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	57	2018
Automatic Gradient Boosting J Thomas, S Coors, B Bischl ICML AutoML Workshop, 2018	30	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	26*	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	26	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	26	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	19	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	16	2019
Automatic exploration of machine learning experiments on openml D Kühn, P Probst, J Thomas, B Bischl arXiv preprint arXiv:1806.10961, 2018	12	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	12	2017
Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019	11	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	9	2018
Deep Semi-supervised Learning for Time-Series Classification J Goschenhofer Deep Learning Applications, Volume 4, 361-384, 2022	8	2022
mlr Tutorial J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ... arXiv preprint arXiv:1609.06146, 2016	8	2016
Towards human centered AutoML F Pfisterer, J Thomas, B Bischl arXiv preprint arXiv:1911.02391, 2019	6	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	6
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	3	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	3	2021

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