Giuseppe Casalicchio
Orcid: 0000-0001-5324-5966
According to our database1,
Giuseppe Casalicchio
authored at least 37 papers
between 2016 and 2024.
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Bibliography
2024
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration.
CoRR, 2024
2023
CoRR, 2023
Proceedings of the Explainable Artificial Intelligence, 2023
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.
Proceedings of the Explainable Artificial Intelligence, 2023
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023
Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation.
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.
WIREs Data Mining Knowl. Discov., 2022
Data Min. Knowl. Discov., 2022
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.
CoRR, 2022
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, 2022
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.
CoRR, 2021
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT).
CoRR, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
2020
Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach.
CoRR, 2020
Proceedings of the ECML PKDD 2020 Workshops, 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.
Proceedings of the xxAI - Beyond Explainable AI, 2020
2019
On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models.
PhD thesis, 2019
J. Open Source Softw., 2019
Comput. Stat., 2019
Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.
CoRR, 2019
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019
2018
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018
2017
CoRR, 2017
2016