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Giuseppe Casalicchio
Giuseppe Casalicchio
Postdoctoral Researcher, LMU Munich, Munich Center for Machine Learning
Bestätigte E-Mail-Adresse bei campus.lmu.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
1520*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
9532016
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
6902020
iml: An R package for interpretable machine learning
C Molnar, G Casalicchio, B Bischl
Journal of Open Source Software 3 (26), 786, 2018
4152018
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
345*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
2832019
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
2102020
OpenML Benchmarking Suites
B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ...
NeurIPS 2021 Datasets and Benchmarks Track, 2021
160*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
105*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
1032024
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
892023
OpenML benchmarking suites and the OpenML100
B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ...
stat 1050, 11, 2017
822017
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
77*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
722019
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
592020
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
562022
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
462019
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
392013
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
342022
Multilabel classification with R package mlr
P Probst, Q Au, G Casalicchio, C Stachl, B Bischl
The R Journal 9 (1), 352-369, 2017
342017
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