Janek Thomas

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Bernd BischlChair of Statistical Learning and Data Science, LMU MunichEmail yang diverifikasi di stat.uni-muenchen.de
Jakob RichterTU DortmundEmail yang diverifikasi di statistik.tu-dortmund.de
Michel LangTU Dortmund; LMU MunichEmail yang diverifikasi di statistik.tu-dortmund.de
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichEmail yang diverifikasi di campus.lmu.de
Florian Pfistererhemato GmbH, Ludwig Maximilian University of MunichEmail yang diverifikasi di stat.uni-muenchen.de
Andreas MayrDept. of Medical Biometry and Statistics, University of MarburgEmail yang diverifikasi di uni-marburg.de
Pieter GijsbersEindhoven University of TechnologyEmail yang diverifikasi di tue.nl
Joaquin VanschorenEindhoven University of TechnologyEmail yang diverifikasi di tue.nl
Daniel HornStatistical Methods for Big Data, TU DortmundEmail yang diverifikasi di tu-dortmund.de
Julia MoosbauerPhD Student, Ludwig-Maximilians-University MunichEmail yang diverifikasi di stat.uni-muenchen.de
Jakob BossekPaderborn UniversityEmail yang diverifikasi di uni-paderborn.de
Florian PargentPsychological Methods and Assessment, LMU MunichEmail yang diverifikasi di psy.lmu.de
Jann GoschenhoferPhD Candidate, LMUEmail yang diverifikasi di stat.uni-muenchen.de
Tobias HeppDepartment of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-NürnbergEmail yang diverifikasi di fau.de
Adam D. SmithU.S. Motus Director, American Bird ConservancyEmail yang diverifikasi di abcbirds.org
Franz MJ PfisterData Science, LMUEmail yang diverifikasi di campus.lmu.de
Kamer Ali YUKSELHead of AI Lab @ aiXplainEmail yang diverifikasi di badehotels.com
Philipp ProbstEmail yang diverifikasi di digital-ratio.de
Enrico RukzioUlm UniversityEmail yang diverifikasi di uni-ulm.de
Michael RietzlerUlm UniversityEmail yang diverifikasi di uni-ulm.de

Ikuti

Janek Thomas

Ludwig-Maximilians-University Munich

Email yang diverifikasi di stat.uni-muenchen.de - Beranda

Artificial Intelligence Machine Learning AutoML Statistics Optimization


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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 13 (2 …, 2023	618	2023
An Open Source AutoML Benchmark P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren ICML AutoML Workshop, 2019	322	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	222*	2017
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	153*	2022
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	88	2018
Amlb: an automl benchmark P Gijsbers, MLP Bueno, S Coors, E LeDell, S Poirier, J Thomas, B Bischl, ... Journal of Machine Learning Research 25 (101), 1-65, 2024	84	2024
Multi-objective hyperparameter optimization in machine learning—An overview F Karl, T Pielok, J Moosbauer, F Pfisterer, S Coors, M Binder, L Schneider, ... ACM Transactions on Evolutionary Learning and Optimization 3 (4), 1-50, 2023	72	2023
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	66	2020
Automatic Gradient Boosting J Thomas, S Coors, B Bischl ICML AutoML Workshop, 2018	36	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	35	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	28	2019
Deep semi-supervised learning for time-series classification J Goschenhofer Deep Learning Applications, Volume 4, 361-384, 2022	22	2022
Multi-objective automatic machine learning with autoxgboostmc F Pfisterer, S Coors, J Thomas, B Bischl arXiv preprint arXiv:1908.10796, 2019	20	2019
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
Towards human centered AutoML F Pfisterer, J Thomas, B Bischl arXiv preprint arXiv:1911.02391, 2019	17	2019
Automatic exploration of machine learning experiments on openml D Kühn, P Probst, J Thomas, B Bischl arXiv preprint arXiv:1806.10961, 2018	17	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	17	2017
Hyperparameter optimization: Foundations, algorithms, best practices and open challenges. arXiv B Bischl, M Binder, M Lang, T Pielok, J Richter, S Coors, J Thomas, ... arXiv preprint arXiv:2107.05847, 2021	12	2021
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	12	2018
Structured verification of machine learning models in industrial settings SR Kaminwar, J Goschenhofer, J Thomas, I Thon, B Bischl Big Data 11 (3), 181-198, 2023	11	2023

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