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. JonesUniversity of WashingtonVerified email at uw.edu
Erich StuderusLecturer and postdoctoral researcher at the University of Basel, Department of PsychologyVerified email at unibas.ch
Matthias FeurerMachine Learning group, University of FreiburgVerified email at informatik.uni-freiburg.de
Andreas BenderLudwig-Maximilians-Universität MünchenVerified email at stat.uni-muenchen.de
Quay AuMember of "Computational Statistics" Group, LMU MunichVerified 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 BossekWestfälische Wilhelms Universität MünsterVerified email at wi.uni-muenster.de
Helmut KüchenhoffInstitut für Statistik, LMU MünchenVerified email at stat.uni-muenchen.de
Pascal KerschkeProfessor of Big Data Analytics in Transportation, TU Dresden, GermanyVerified email at tu-dresden.de
Benjamin HofnerHead of Section Biostatistics, Paul-Ehrlich-InstituteVerified email at pei.de
Luk BruyneelOnafhankelijke Ziekefondsen/Mutalités Libres, KU Leuven University of LeuvenVerified email at mloz.be
Anne-Laure BoulesteixLudwig-Maximilians-Universität MünchenVerified email at ibe.med.uni-muenchen.de
Gerhard TutzVerified 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 R 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	607*	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	510	2016
iml: An R package for interpretable machine learning C Molnar, G Casalicchio, B Bischl Journal of Open Source Software 3 (26), 786, 2018	103	2018
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	47	2018
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050, 11, 2017	45*	2017
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	35	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	22	2013
Quantifying model complexity via functional decomposition for better post-hoc interpretability C Molnar, G Casalicchio, B Bischl Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019	21*	2019
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	20	2019
Multilabel classification with R package mlr P Probst, Q Au, G Casalicchio, C Stachl, B Bischl arXiv preprint arXiv:1703.08991, 2017	18	2017
Interpretable Machine Learning--A Brief History, State-of-the-Art and Challenges C Molnar, G Casalicchio, B Bischl arXiv preprint arXiv:2010.09337, 2020	10	2020
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	10	2017
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	8	2020
Sampling, intervention, prediction, aggregation: A generalized framework for model-agnostic interpretations CA Scholbeck, C Molnar, C Heumann, B Bischl, G Casalicchio Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019	8	2019
Subject-specific Bradley–Terry–Luce models with implicit variable selection G Casalicchio, G Tutz, G Schauberger Statistical Modelling 15 (6), 526-547, 2015	8	2015
Pitfalls to avoid when interpreting machine learning models C Molnar, G König, J Herbinger, T Freiesleben, S Dandl, CA Scholbeck, ... arXiv preprint arXiv:2007.04131, 2020	7	2020
ordBTL: Modelling comparison data with ordinal response G Casalicchio R package version 0.8, 2013	7	2013
The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models G Casalicchio, B Bischl, AL Boulesteix, M Schmid Biometrics 72 (2), 392-401, 2016	5	2016
Climate parameters and the influence on the footpad health status of fattening turkeys BUT 6 during the early rearing phase. N Ziegler, S Bergmann, J Hübel, T Bartels, C Schumacher, A Bender, ... Berliner und Münchener Tierärztliche Wochenschrift 126 (5/6), 181-188, 2013	5*	2013
On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models G Casalicchio lmu, 2019	1	2019

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