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
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Zachary M. JonesMetaVerified email at fb.com
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
Erich StuderusLecturer and postdoctoral researcher at the University of Basel, Department of PsychologyVerified email at unibas.ch
Christian A. ScholbeckResearch Associate Ludwig-Maximilians-Universität MunichVerified email at stat.uni-muenchen.de
Matthias FeurerMachine Learning group, University of FreiburgVerified email at informatik.uni-freiburg.de
Andreas BenderLudwig-Maximilians-Universität 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
Jakob BossekRWTH Aachen UniversityVerified email at aim.rwth-aachen.de
Helmut KüchenhoffInstitut für Statistik, LMU MünchenVerified email at stat.uni-muenchen.de
Clemens StachlAssociate Professor of Behavioral Science, University of St. GallenVerified email at unisg.ch
Stefan CoorsComputational Statistics, Ludwig-Maximilians Unversity MunichVerified email at campus.lmu.de
Pascal KerschkeProfessor of Big Data Analytics in Transportation, TU Dresden, GermanyVerified email at tu-dresden.de

Giuseppe Casalicchio

Postdoctoral Researcher, LMU Munich, Munich Center for Machine Learning

Verified email at campus.lmu.de - Homepage

Machine Learning Statistics 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	895*	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	703	2016
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	219	2018
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	148	2020
Visualizing the feature importance for black box models G Casalicchio, C Molnar, B Bischl Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018	111	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	99	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 (3), 977-991, 2019	50	2019
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050, 11, 2017	50	2017
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	48*	2019
OpenML Benchmarking Suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... NeurIPS 2021 Datasets and Benchmarks Track, 2021	37*	2021
Model-agnostic Feature Importance and Effects with Dependent Features--A Conditional Subgroup Approach C Molnar, G König, B Bischl, G Casalicchio arXiv preprint arXiv:2006.04628, 2020	31	2020
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	30	2013
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	27	2020
Multilabel classification with R package mlr P Probst, Q Au, G Casalicchio, C Stachl, B Bischl The R Journal 9 (1), 352-369, 2017	22	2017
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	18	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 …, 2022	17	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	16	2017
Subject-specific Bradley–Terry–Luce models with implicit variable selection G Casalicchio, G Tutz, G Schauberger Statistical Modelling 15 (6), 526-547, 2015	9	2015
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	8*	2021
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	8	2021

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