Giuseppe Casalicchio

According to our database, Giuseppe Casalicchio authored at least 45 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Correction: Marginal effects for non-linear prediction functions.

Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann

Data Min. Knowl. Discov., November, 2024

Marginal effects for non-linear prediction functions.

Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann

Data Min. Knowl. Discov., September, 2024

Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach.

Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio

Data Min. Knowl. Discov., September, 2024

Efficient and Accurate Explanation Estimation with Distribution Compression.

Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek

CoRR, 2024

Effector: A Python package for regional explanations.

Vasilis Gkolemis, Christos Diou, Eirini Ntoutsi, Theodore Dalamagas, Bernd Bischl, Julia Herbinger, Giuseppe Casalicchio

CoRR, 2024

Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration.

Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio

CoRR, 2024

A Guide to Feature Importance Methods for Scientific Inference.

Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König

Proceedings of the Explainable Artificial Intelligence, 2024

mlr3summary: Concise and interpretable summaries for machine learning models.

Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann

Proceedings of the Joint Proceedings of the xAI 2024 Late-breaking Work, 2024

On the Robustness of Global Feature Effect Explanations.

Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Position: Why We Must Rethink Empirical Research in Machine Learning.

Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis.

Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann

CoRR, 2023

fmeffects: An R Package for Forward Marginal Effects.

Holger Löwe, Christian A. Scholbeck, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio

CoRR, 2023

Decomposing Global Feature Effects Based on Feature Interactions.

Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio

CoRR, 2023

counterfactuals: An R Package for Counterfactual Explanation Methods.

Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio

CoRR, 2023

Algorithm-Agnostic Feature Attributions for Clustering.

Christian A. Scholbeck, Henri Funk, Giuseppe Casalicchio

Proceedings of the Explainable Artificial Intelligence, 2023

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.

Christoph Molnar, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl

Proceedings of the Explainable Artificial Intelligence, 2023

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations.

Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation.

Julia Herbinger, Susanne Dandl, Fiona Katharina Ewald, Sofia Loibl, Giuseppe Casalicchio

Proceedings of the Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30, 2023

2022

Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results.

Christina Nießl, Moritz Herrmann, Chiara Wiedemann, Giuseppe Casalicchio, Anne-Laure Boulesteix

WIREs Data Mining Knowl. Discov., 2022

Grouped feature importance and combined features effect plot.

Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio

Data Min. Knowl. Discov., 2022

Algorithm-Agnostic Interpretations for Clustering.

Christian A. Scholbeck, Henri Funk, Giuseppe Casalicchio

CoRR, 2022

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.

Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

CoRR, 2022

Developing Open Source Educational Resources for Machine Learning and Data Science.

Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl

Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, 2022

REPID: Regional Effect Plots with implicit Interaction Detection.

Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.

Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl

CoRR, 2021

Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT).

Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup

CoRR, 2021

Explaining Hyperparameter Optimization via Partial Dependence Plots.

Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

OpenML Benchmarking Suites.

Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

Pitfalls to Avoid when Interpreting Machine Learning Models.

Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl

CoRR, 2020

Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges.

Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl

Proceedings of the ECML PKDD 2020 Workshops, 2020

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.

Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl

Proceedings of the xxAI - Beyond Explainable AI, 2020

2019

mlr3: A modern object-oriented machine learning framework in R.

Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl

Dataset, December, 2019

On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models.

Giuseppe Casalicchio

PhD thesis, 2019

mlr3: A modern object-oriented machine learning framework in R.

Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl

J. Open Source Softw., 2019

OpenML: An R package to connect to the machine learning platform OpenML.

Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl

Comput. Stat., 2019

Component-Wise Boosting of Targets for Multi-Output Prediction.

Quay Au, Daniel Schalk, Giuseppe Casalicchio, Ramona Schödel, Clemens Stachl, Bernd Bischl

CoRR, 2019

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.

Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl

CoRR, 2019

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations.

Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability.

Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018

iml: An R package for Interpretable Machine Learning.

Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl

J. Open Source Softw., 2018

Visualizing the Feature Importance for Black Box Models.

Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

2017

Multilabel Classification with R Package mlr.

Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl

R J., 2017

OpenML Benchmarking Suites and the OpenML100.

Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren

CoRR, 2017

OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML.

Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl

CoRR, 2017

2016

mlr: Machine Learning in R.

Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones

J. Mach. Learn. Res., 2016