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
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Christian A. ScholbeckLudwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Quay AuMember of "Computational Statistics" Group, LMU MunichVerified email at stat.uni-muenchen.de
Gunnar KönigPostdoctoral Researcher, University of TübingenVerified email at uni-tuebingen.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
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br
Christian HeumannProfessor Statistik, Ludwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
Matthias FeurerLMU MunichVerified email at stat.uni-muenchen.de
Frank HutterELLIS Institute & Professor, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Clemens StachlInstitute of Behavioral Science and Technology, University of St. GallenVerified email at unisg.ch
Andreas BenderLudwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
Jakob BossekPaderborn UniversityVerified email at uni-paderborn.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	1508*	2015
mlr: Machine Learning in R B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ... Journal of Machine Learning Research 17 (170), 1-5, 2016	950	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	685	2020
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	410	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	341*	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	279	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	206	2020
OpenML Benchmarking Suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... NeurIPS 2021 Datasets and Benchmarks Track, 2021	156*	2021
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	104*	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 38 (5), 2903-2941, 2024	102	2024
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	81	2023
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050, 11, 2017	81	2017
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	76*	2021
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	72	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	59	2020
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	52	2022
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	46	2019
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	39	2013
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	34	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	34	2017

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