Giuseppe Casalicchio
Giuseppe Casalicchio
Postdoctoral Researcher, LMU Munich, Munich Center for Machine Learning
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Zitiert von
Zitiert von
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
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
Interpretable machine learning–a brief history, state-of-the-art and challenges
C Molnar, G Casalicchio, B Bischl
ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine …, 2021
iml: An R package for interpretable machine learning
C Molnar, G Casalicchio, B Bischl
Journal of Open Source Software 3 (26), 786, 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
Visualizing the feature importance for black box models
G Casalicchio, C Molnar, B Bischl
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019
OpenML Benchmarking Suites
B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ...
NeurIPS 2021 Datasets and Benchmarks Track, 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
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
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
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
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
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
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
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
Multilabel classification with R package mlr
P Probst, Q Au, G Casalicchio, C Stachl, B Bischl
The R Journal 9 (1), 352-369, 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
Grouped feature importance and combined features effect plot
Q Au, J Herbinger, C Stachl, B Bischl, G Casalicchio
arXiv preprint arXiv:2104.11688, 2021
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
Subject-specific Bradley–Terry–Luce models with implicit variable selection
G Casalicchio, G Tutz, G Schauberger
Statistical Modelling 15 (6), 526-547, 2015
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