Giuseppe Casalicchio

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Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Christoph MolnarIndependent researcher and book authorVerified email at christophmolnar.com
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
Christian A. ScholbeckResearch Associate at Ludwig-Maximilians-Universität (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
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Erich StuderusLecturer and researcher at the University of Basel, Department of PsychologyVerified email at unibas.ch
Clemens StachlInstitute of Behavioral Science and Technology, University of St. GallenVerified email at unisg.ch
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br
Andreas BenderLudwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
Christian HeumannProfessor Statistik, Ludwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
Jakob BossekPaderborn UniversityVerified email at uni-paderborn.de
Helmut KüchenhoffInstitut für Statistik, LMU MünchenVerified email at stat.uni-muenchen.de
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.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	1235*	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	850	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	434	2020
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
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	223*	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	214	2019
OpenML Benchmarking Suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... NeurIPS 2021 Datasets and Benchmarks Track, 2021	151*	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	91	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	80*	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	66	2023
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
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	49	2020
Relating the partial dependence plot and permutation feature importance to the data generating process C Molnar, T Freiesleben, G König, J Herbinger, T Reisinger, ... World Conference on Explainable Artificial Intelligence, 456-479, 2023	41	2023
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	41*	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	34	2013
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	33	2019
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	29	2022
Multilabel classification with R package mlr P Probst, Q Au, G Casalicchio, C Stachl, B Bischl The R Journal 9 (1), 352-369, 2017	28	2017
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
Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results C Nießl, M Herrmann, C Wiedemann, G Casalicchio, AL Boulesteix Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12 (2 …, 2022	16	2022

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