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)

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)

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)

Multilabel Classification with R Package mlr. - Philipp Probst; Quay Au; Giuseppe Casalicchio; Clemens Stachl; Bernd Bischl (R J., 2017)

Visualizing the Feature Importance for Black Box Models. - Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl (ECML/PKDD (1), 2018)

iml: An R package for Interpretable Machine Learning. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (J. Open Source Softw., 2018)

Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (PKDD/ECML Workshops (1), 2019)

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. - Christian A. Scholbeck; Christoph Molnar; Christian Heumann; Bernd Bischl; Giuseppe Casalicchio (PKDD/ECML Workshops (1), 2019)

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (CoRR, 2019)

Component-Wise Boosting of Targets for Multi-Output Prediction. - Quay Au; Daniel Schalk; Giuseppe Casalicchio; Ramona Schödel; Clemens Stachl; Bernd Bischl (CoRR, 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)

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)

On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models. - Giuseppe Casalicchio (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 (Zenodo, 2019)

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 (xxAI@ICML, 2020)

Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (PKDD/ECML Workshops, 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)

OpenML Benchmarking Suites. - Bernd Bischl; Giuseppe Casalicchio; Matthias Feurer; Pieter Gijsbers; Frank Hutter; Michel Lang; Rafael Gomes Mantovani; Jan N. van Rijn; Joaquin Vanschoren (NeurIPS Datasets and Benchmarks, 2021)

Explaining Hyperparameter Optimization via Partial Dependence Plots. - Julia Moosbauer; Julia Herbinger; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (NeurIPS, 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)

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)

REPID: Regional Effect Plots with implicit Interaction Detection. - Julia Herbinger; Bernd Bischl; Giuseppe Casalicchio (AISTATS, 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 (Teaching ML, 2022)

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. - Julia Moosbauer; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (CoRR, 2022)

Algorithm-Agnostic Interpretations for Clustering. - Christian A. Scholbeck; Henri Funk; Giuseppe Casalicchio (CoRR, 2022)

Grouped feature importance and combined features effect plot. - Quay Au; Julia Herbinger; Clemens Stachl; Bernd Bischl; Giuseppe Casalicchio (Data Min. Knowl. Discov., 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)

Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. - Julia Herbinger; Susanne Dandl; Fiona Katharina Ewald; Sofia Loibl; Giuseppe Casalicchio (ECAI Workshops (1), 2023)

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. - Susanne Dandl; Giuseppe Casalicchio; Bernd Bischl; Ludwig Bothmann (ECML/PKDD (3), 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 (xAI (1), 2023)

Algorithm-Agnostic Feature Attributions for Clustering. - Christian A. Scholbeck; Henri Funk; Giuseppe Casalicchio (xAI (1), 2023)

counterfactuals: An R Package for Counterfactual Explanation Methods. - Susanne Dandl; Andreas Hofheinz; Martin Binder; Bernd Bischl; Giuseppe Casalicchio (CoRR, 2023)

Decomposing Global Feature Effects Based on Feature Interactions. - Julia Herbinger; Bernd Bischl; Giuseppe Casalicchio (CoRR, 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)

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 (ICML, 2024)

On the Robustness of Global Feature Effect Explanations. - Hubert Baniecki; Giuseppe Casalicchio; Bernd Bischl; Przemyslaw Biecek (ECML/PKDD (2), 2024)

mlr3summary: Concise and interpretable summaries for machine learning models. - Susanne Dandl; Marc Becker; Bernd Bischl; Giuseppe Casalicchio; Ludwig Bothmann (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)

A Guide to Feature Importance Methods for Scientific Inference. - Fiona Katharina Ewald; Ludwig Bothmann; Marvin N. Wright; Bernd Bischl; Giuseppe Casalicchio; Gunnar König (xAI (2), 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 001; Conor J. Walsh; Giuseppe Casalicchio (CoRR, 2024)

Effector: A Python package for regional explanations. - Vasilis Gkolemis; Christos Diou; Eirini Ntoutsi; Theodore Dalamagas 001; Bernd Bischl; Julia Herbinger; Giuseppe Casalicchio (CoRR, 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., 2024)

Marginal effects for non-linear prediction functions. - Christian A. Scholbeck; Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl; Christian Heumann (Data Min. Knowl. Discov., 2024)

Correction: Marginal effects for non-linear prediction functions. - Christian A. Scholbeck; Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl; Christian Heumann (Data Min. Knowl. Discov., 2024)

Efficient and Accurate Explanation Estimation with Distribution Compression. - Hubert Baniecki; Giuseppe Casalicchio; Bernd Bischl; Przemyslaw Biecek (ICLR, 2025)

OpenML: Insights from 10 years and more than a thousand papers. - Bernd Bischl; Giuseppe Casalicchio; Taniya Das; Matthias Feurer; Sebastian Fischer; Pieter Gijsbers; Subhaditya Mukherjee; Andreas C. Müller 002; László Németh; Luis Oala; Lennart Purucker; Sahithya Ravi; et al. (Patterns, 2025)

fmeffects: An R Package for Forward Marginal Effects. - Holger Löwe; Christian A. Scholbeck; Christian Heumann; Bernd Bischl; Giuseppe Casalicchio (R J., 2025)

Privilege Scores. - Ludwig Bothmann; Philip A. Boustani; José M. Álvarez 002; Giuseppe Casalicchio; Bernd Bischl; Susanne Dandl (CoRR, 2025)