Bernd Bischl

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Citations	7773	6725
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Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningVerified email at campus.lmu.de
Janek ThomasLudwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Olaf MersmannTH KölnVerified email at p-value.net
Heike TrautmannProfessor of Data Science: Statistics and Optimization, Department of Information SystemsVerified email at wi.uni-muenster.de
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Clemens StachlAssociate Professor of Behavioral Science, University of St. GallenVerified email at unisg.ch
Quay AuMember of "Computational Statistics" Group, LMU MunichVerified email at stat.uni-muenchen.de
David RügamerInterim Professor for Computational Statistics, TU DortmundVerified email at stat.uni-muenchen.de
Daniel HornStatistical Methods for Big Data, TU DortmundVerified email at tu-dortmund.de
Xudong SunKTH Royal Institute of TechnologyVerified email at kth.se
Jörg RahnenführerTU Dortmund UniversityVerified email at statistik.tu-dortmund.de
Jakob BossekRWTH Aachen UniversityVerified email at aim.rwth-aachen.de
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Philipp ProbstVerified email at digital-ratio.de
Mike PreussUniversiteit LeidenVerified email at liacs.leidenuniv.nl

Bernd Bischl

Chair of Statistical Learning and Data Science, LMU Munich

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

Machine Learning Statistics Data Science Statistical Learning


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OpenML: networked science in machine learning J Vanschoren, JN Van Rijn, B Bischl, L Torgo ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014	1012	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	740	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	471	2019
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	335	2020
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	307	2011
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	259	2018
Interpretable machine learning–a brief history, state-of-the-art and challenges C Molnar, G Casalicchio, B Bischl ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine …, 2021	243	2021
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	212	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	205*	2020
Amlb: an automl benchmark P Gijsbers, MLP Bueno, S Coors, E LeDell, S Poirier, J Thomas, B Bischl, ... arXiv preprint arXiv:2207.12560, 2022	196*	2022
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	184	2012
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	172	2017
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	153	2012
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	151	2019
Visualizing the feature importance for black box models G Casalicchio, C Molnar, B Bischl Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019	148	2019
Multi-objective counterfactual explanations S Dandl, C Molnar, M Binder, B Bischl Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference …, 2020	138	2020
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	131	2017
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	105	2015
Openml benchmarking suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... arXiv preprint arXiv:1708.03731, 2017	103	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	92	2013

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