Giuseppe Casalicchio

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Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Quay AuMember of "Computational Statistics" Group, LMU MunichVerified email at stat.uni-muenchen.de
Gunnar KönigPhD Candidate, LMU Munich/MCML; Research Fellow, University of ViennaVerified email at stat.uni-muenchen.de
Zachary M. JonesMetaVerified email at fb.com
Christian A. ScholbeckResearch Associate at Ludwig-Maximilians-Universität (LMU) MunichVerified email at stat.uni-muenchen.de
Erich StuderusLecturer and researcher at the University of Basel, Department of PsychologyVerified email at unibas.ch
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Clemens StachlAssociate Professor of Behavioral Science, University of St. GallenVerified email at unisg.ch
Andreas BenderLudwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
Jakob BossekRWTH Aachen UniversityVerified email at aim.rwth-aachen.de
Helmut KüchenhoffInstitut für Statistik, LMU MünchenVerified email at stat.uni-muenchen.de
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Matthias FeurerLMU Munich & Munich Center for Machine LearningVerified email at stat.uni-muenchen.de
Christian HeumannProfessor Statistik, Ludwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de

Giuseppe Casalicchio

Postdoctoral Researcher, LMU Munich, Munich Center for Machine Learning

Verified email at campus.lmu.de - Homepage

Machine Learning xAI Explainable AI interpretability Interpretable Machine Learning


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OpenML: A networked science platform for machine learning J Vanschoren, JN van Rijn, B Bischl, G Casalicchio, M Lang, M Feurer 2015 ICML Workshop on Machine Learning Open Source Software (MLOSS 2015)., 2015	1185*	2015
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	825	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	394	2020
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	310	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	204	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	199	2019
OpenML Benchmarking Suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... NeurIPS 2021 Datasets and Benchmarks Track, 2021	128*	2021
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	76	2020
Quantifying model complexity via functional decomposition for better post-hoc interpretability C Molnar, G Casalicchio, B Bischl Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, 193--204, 2019	75*	2019
OpenML: An R package to connect to the machine learning platform OpenML G Casalicchio, J Bossek, M Lang, D Kirchhoff, P Kerschke, B Hofner, ... Computational Statistics 34, 977-991, 2019	65	2019
Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach C Molnar, G König, B Bischl, G Casalicchio Data Mining and Knowledge Discovery, 1-39, 2023	55	2023
Pitfalls to avoid when interpreting machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... Accepted at the ICML 2020 workshop XXAI: Extending Explainable AI Beyond …, 2020	47	2020
Explaining hyperparameter optimization via partial dependence plots J Moosbauer, J Herbinger, G Casalicchio, M Lindauer, B Bischl Advances in Neural Information Processing Systems 34, 2280-2291, 2021	38*	2021
Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase S Bergmann, N Ziegler, T Bartels, J Hübel, C Schumacher, E Rauch, ... Poultry science 92 (5), 1171-1176, 2013	32	2013
Relating the partial dependence plot and permutation feature importance to the data generating process C Molnar, T Freiesleben, G König, G Casalicchio, MN Wright, B Bischl arXiv preprint arXiv:2109.01433, 2021	31	2021
Sampling, intervention, prediction, aggregation: A generalized framework for model-agnostic interpretations CA Scholbeck, C Molnar, C Heumann, B Bischl, G Casalicchio Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, 205--216, 2019	29	2019
mlr3: A modern object-oriented machine learning framework in RJ Open Source Softw M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ...	27	2019
Multilabel classification with R package mlr P Probst, Q Au, G Casalicchio, C Stachl, B Bischl The R Journal 9 (1), 352-369, 2017	26	2017
Grouped feature importance and combined features effect plot Q Au, J Herbinger, C Stachl, B Bischl, G Casalicchio Data Mining and Knowledge Discovery 36 (4), 1401-1450, 2022	23	2022
Nonlinear analysis to detect if excellent nursing work environments have highest well‐being G Casalicchio, E Lesaffre, H Küchenhoff, L Bruyneel Journal of nursing scholarship 49 (5), 537-547, 2017	18	2017

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