Bernd Bischl

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Citations	15157	12664
h-index	54	45
i10-index	132	122

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Giuseppe CasalicchioSenior Lecturer and Researcher, LMU Munich, Munich Center for Machine LearningVerified email at campus.lmu.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Janek ThomasLudwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de
Florian Pfistererhemato GmbH, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
David RügamerProfessor at LMU Munich, PI at Munich Center for Machine LearningVerified email at stat.uni-muenchen.de
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Christoph MolnarIndependent researcher and book authorVerified email at christophmolnar.com
Olaf MersmannTH KölnVerified email at p-value.net
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Heike TrautmannProfessor of Machine Learning and Optimisation, Paderborn University, GermanyVerified email at uni-paderborn.de
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Daniel HornStatistical Methods for Big Data, TU DortmundVerified email at tu-dortmund.de
Quay AuMember of "Computational Statistics" Group, LMU MunichVerified email at stat.uni-muenchen.de
Clemens StachlInstitute of Behavioral Science and Technology, University of St. GallenVerified email at unisg.ch
Susanne DandlUniversität Zürich (UZH)Verified email at uzh.ch
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
Jörg RahnenführerTU Dortmund UniversityVerified email at statistik.tu-dortmund.de
Julia MoosbauerPhD Student, Ludwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de

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 Scientific Software


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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	1629	2014
Tunability: Importance of hyperparameters of machine learning algorithms P Probst, AL Boulesteix, B Bischl Journal of Machine Learning Research 20 (53), 1-32, 2019	1012	2019
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	960	2016
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	760	2020
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	718	2020
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	622	2023
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	440	2018
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	440	2011
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	402*	2024
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	378	2019
Multi-objective counterfactual explanations S Dandl, C Molnar, M Binder, B Bischl International conference on parallel problem solving from nature, 448-469, 2020	373	2020
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	371	2020
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	308	2016
Visualizing the feature importance for black box models G Casalicchio, C Molnar, B Bischl Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019	302	2019
General pitfalls of model-agnostic interpretation methods for machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... International Workshop on Extending Explainable AI Beyond Deep Models and …, 2020	292*	2020
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	239*	2017
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	223	2012
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	197	2015
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	184	2012
Openml benchmarking suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... arXiv preprint arXiv:1708.03731, 2017	176	2017

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