Bernd Bischl

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Michel LangTU Dortmund; LMU MunichAdresse e-mail validée de statistik.tu-dortmund.de
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningAdresse e-mail validée de campus.lmu.de
Joaquin VanschorenEindhoven University of TechnologyAdresse e-mail validée de tue.nl
Janek ThomasLudwig-Maximilians-University MunichAdresse e-mail validée de stat.uni-muenchen.de
Olaf MersmannTH KölnAdresse e-mail validée de p-value.net
Jan N. van RijnLeiden UniversityAdresse e-mail validée de liacs.leidenuniv.nl
Lars KotthoffUniversity of WyomingAdresse e-mail validée de uwyo.edu
Jakob RichterTU DortmundAdresse e-mail validée de statistik.tu-dortmund.de
Heike TrautmannProfessor of Data Science: Statistics and Optimization, Department of Information SystemsAdresse e-mail validée de wi.uni-muenster.de
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichAdresse e-mail validée de stat.uni-muenchen.de
Clemens StachlAssociate Professor of Behavioral Science, University of St. GallenAdresse e-mail validée de unisg.ch
Quay AuMember of "Computational Statistics" Group, LMU MunichAdresse e-mail validée de stat.uni-muenchen.de
Daniel HornStatistical Methods for Big Data, TU DortmundAdresse e-mail validée de tu-dortmund.de
Jörg RahnenführerTU Dortmund UniversityAdresse e-mail validée de statistik.tu-dortmund.de
Xudong SunKTH Royal Institute of TechnologyAdresse e-mail validée de kth.se
Jakob BossekRWTH Aachen UniversityAdresse e-mail validée de aim.rwth-aachen.de
Luís TorgoCanada Research Chair, Professor, Faculty of Computer Science, Dalhousie UniversityAdresse e-mail validée de dal.ca
Philipp ProbstAdresse e-mail validée de digital-ratio.de
David RügamerInterim Professor for Computational Statistics, RWTH AachenAdresse e-mail validée de stat.uni-muenchen.de
Mike PreussUniversiteit LeidenAdresse e-mail validée de liacs.leidenuniv.nl

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Bernd Bischl

Chair of Statistical Learning and Data Science, LMU Munich

Adresse e-mail validée de stat.uni-muenchen.de - Page d'accueil

Machine Learning Statistics Data Science Statistical Learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
OpenML: networked science in machine learning J Vanschoren, JN Van Rijn, B Bischl, L Torgo ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014	901	2014
mlr: Machine Learning in R B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ... The Journal of Machine Learning Research 17 (1), 5938-5942, 2016	685	2016
Tunability: Importance of hyperparameters of machine learning algorithms P Probst, AL Boulesteix, B Bischl The Journal of Machine Learning Research 20 (1), 1934-1965, 2019	381	2019
Exploratory landscape analysis O Mersmann, B Bischl, H Trautmann, M Preuss, C Weihs, G Rudolph Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011	275	2011
Benchmark for filter methods for feature selection in high-dimensional classification data A Bommert, X Sun, B Bischl, J Rahnenführer, M Lang Computational Statistics & Data Analysis 143, 106839, 2020	263	2020
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	222	2018
Aslib: A benchmark library for algorithm selection B Bischl, P Kerschke, L Kotthoff, M Lindauer, Y Malitsky, A Fréchette, ... Artificial Intelligence 237, 41-58, 2016	200	2016
Predicting personality from patterns of behavior collected with smartphones C Stachl, Q Au, R Schoedel, SD Gosling, GM Harari, D Buschek, ... Proceedings of the National Academy of Sciences 117 (30), 17680-17687, 2020	183*	2020
Resampling methods for meta-model validation with recommendations for evolutionary computation B Bischl, O Mersmann, H Trautmann, C Weihs Evolutionary computation 20 (2), 249-275, 2012	171	2012
An open source AutoML benchmark P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren arXiv preprint arXiv:1907.00909, 2019	164	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	156	2017
Interpretable machine learning–a brief history, state-of-the-art and challenges C Molnar, G Casalicchio, B Bischl Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020	154	2020
Algorithm selection based on exploratory landscape analysis and cost-sensitive learning B Bischl, O Mersmann, H Trautmann, M Preuß Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	144	2012
Personality traits predict smartphone usage C Stachl, S Hilbert, JQ Au, D Buschek, A De Luca, B Bischl, H Hussmann, ... European Journal of Personality 31 (6), 701-722, 2017	123	2017
Visualizing the feature importance for black box models G Casalicchio, C Molnar, B Bischl Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018	113	2018
mlr3: A modern object-oriented machine learning framework in R M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ... Journal of Open Source Software 4 (44), 1903, 2019	101	2019
Multi-objective counterfactual explanations S Dandl, C Molnar, M Binder, B Bischl International Conference on Parallel Problem Solving from Nature, 448-469, 2020	92	2020
Effectiveness of random search in SVM hyper-parameter tuning RG Mantovani, ALD Rossi, J Vanschoren, B Bischl, AC De Carvalho 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015	87	2015
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050, 11, 2017	86*	2017
A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem O Mersmann, B Bischl, H Trautmann, M Wagner, J Bossek, F Neumann Annals of Mathematics and Artificial Intelligence 69 (2), 151-182, 2013	84	2013

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