Bernd Bischl

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Citations	13438	11759
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Giuseppe CasalicchioPostdoctoral 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 PfistererLudwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
David RügamerProfessor of Data Science (LMU Munich, Munich Center for Machine Learning)Verified email at stat.uni-muenchen.de
Christoph MolnarIndependent researcher and book authorVerified email at christophmolnar.com
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Olaf MersmannTH KölnVerified email at p-value.net
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Heike TrautmannProfessor of Machine Learning and Optimisation, Paderborn University, GermanyVerified email at uni-paderborn.de
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Quay AuMember of "Computational Statistics" Group, LMU MunichVerified email at stat.uni-muenchen.de
Daniel HornStatistical Methods for Big Data, TU DortmundVerified email at tu-dortmund.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
Julia MoosbauerPhD Student, Ludwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de
Xudong SunControl and Statistics, Institute of AI for Health, Helmholtz MunichVerified email at kth.se

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	1504	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	925	2016
Tunability: Importance of hyperparameters of machine learning algorithms P Probst, AL Boulesteix, B Bischl Journal of Machine Learning Research 20 (53), 1-32, 2019	875	2019
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	652	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	641	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	433	2023
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	424	2011
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	401	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	364*	2024
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	341*	2020
Multi-objective counterfactual explanations S Dandl, C Molnar, M Binder, B Bischl International conference on parallel problem solving from nature, 448-469, 2020	327	2020
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	326	2019
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	284	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	273	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	252*	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	234	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	223*	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	180	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	178	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	163	2017

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