Bernd Bischl

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Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningAdresse e-mail validée de campus.lmu.de
Michel LangTU Dortmund; LMU MunichAdresse e-mail validée de statistik.tu-dortmund.de
Janek ThomasLudwig-Maximilians-University MunichAdresse e-mail validée de stat.uni-muenchen.de
Joaquin VanschorenEindhoven University of TechnologyAdresse e-mail validée de tue.nl
Florian PfistererLudwig Maximilian University of MunichAdresse e-mail validée de stat.uni-muenchen.de
Jakob RichterTU DortmundAdresse e-mail validée de statistik.tu-dortmund.de
Christoph MolnarIndependent researcher and book authorAdresse e-mail validée de christophmolnar.com
Olaf MersmannTH KölnAdresse e-mail validée de p-value.net
Lars KotthoffUniversity of WyomingAdresse e-mail validée de uwyo.edu
Heike TrautmannProfessor of Machine Learning and Optimisation, Paderborn University, GermanyAdresse e-mail validée de uni-paderborn.de
David RügamerAssociate Professor for Data Science, LMU MunichAdresse e-mail validée de stat.uni-muenchen.de
Jan N. van RijnLeiden UniversityAdresse e-mail validée de liacs.leidenuniv.nl
Daniel HornStatistical Methods for Big Data, TU DortmundAdresse e-mail validée de tu-dortmund.de
Quay AuMember of "Computational Statistics" Group, LMU MunichAdresse e-mail validée de stat.uni-muenchen.de
Clemens StachlInstitute of Behavioral Science and Technology, University of St. GallenAdresse e-mail validée de unisg.ch
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichAdresse e-mail validée de campus.lmu.de
Jakob BossekPaderborn UniversityAdresse e-mail validée de uni-paderborn.de
Xudong SunKTH Royal Institute of TechnologyAdresse e-mail validée de kth.se
Jörg RahnenführerTU Dortmund UniversityAdresse e-mail validée de statistik.tu-dortmund.de
Philipp ProbstAdresse e-mail validée de digital-ratio.de

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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 Scientific Software


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	1256	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	827	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	636	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	454	2020
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	433	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	352	2011
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	326	2018
Amlb: an automl benchmark P Gijsbers, MLP Bueno, S Coors, E LeDell, S Poirier, J Thomas, B Bischl, ... arXiv preprint arXiv:2207.12560, 2022	278*	2022
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	278*	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	240	2016
Multi-objective counterfactual explanations S Dandl, C Molnar, M Binder, B Bischl International Conference on Parallel Problem Solving from Nature, 448-469, 2020	232	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	219	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	213	2019
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	207	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	199*	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	167	2012
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	155	2023
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	150	2017
Openml benchmarking suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... arXiv preprint arXiv:1708.03731, 2017	150	2017
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	139*	2020

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