[{"date_created":"2025-09-11T15:44:54Z","author":[{"first_name":"Maximilian","full_name":"Muschalik, Maximilian","last_name":"Muschalik"},{"first_name":"Fabian","full_name":"Fumagalli, Fabian","last_name":"Fumagalli"},{"full_name":"Frazzetto, Paolo","last_name":"Frazzetto","first_name":"Paolo"},{"full_name":"Strotherm, Janine","last_name":"Strotherm","first_name":"Janine"},{"last_name":"Hermes","full_name":"Hermes, Luca","first_name":"Luca"},{"full_name":"Sperduti, Alessandro","last_name":"Sperduti","first_name":"Alessandro"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"last_name":"Hammer","full_name":"Hammer, Barbara","first_name":"Barbara"}],"date_updated":"2025-09-11T16:14:54Z","title":"Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks","citation":{"short":"M. Muschalik, F. Fumagalli, P. Frazzetto, J. Strotherm, L. Hermes, A. Sperduti, E. Hüllermeier, B. Hammer, in: The Thirteenth International Conference on Learning Representations (ICLR), 2025.","mla":"Muschalik, Maximilian, et al. “Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks.” <i>The Thirteenth International Conference on Learning Representations (ICLR)</i>, 2025.","bibtex":"@inproceedings{Muschalik_Fumagalli_Frazzetto_Strotherm_Hermes_Sperduti_Hüllermeier_Hammer_2025, title={Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks}, booktitle={The Thirteenth International Conference on Learning Representations (ICLR)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Frazzetto, Paolo and Strotherm, Janine and Hermes, Luca and Sperduti, Alessandro and Hüllermeier, Eyke and Hammer, Barbara}, year={2025} }","apa":"Muschalik, M., Fumagalli, F., Frazzetto, P., Strotherm, J., Hermes, L., Sperduti, A., Hüllermeier, E., &#38; Hammer, B. (2025). Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks. <i>The Thirteenth International Conference on Learning Representations (ICLR)</i>.","ieee":"M. Muschalik <i>et al.</i>, “Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks,” 2025.","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Paolo Frazzetto, Janine Strotherm, Luca Hermes, Alessandro Sperduti, Eyke Hüllermeier, and Barbara Hammer. “Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks.” In <i>The Thirteenth International Conference on Learning Representations (ICLR)</i>, 2025.","ama":"Muschalik M, Fumagalli F, Frazzetto P, et al. Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks. In: <i>The Thirteenth International Conference on Learning Representations (ICLR)</i>. ; 2025."},"year":"2025","department":[{"_id":"660"}],"user_id":"93420","_id":"61229","project":[{"name":"TRR 318 - Project Area C","_id":"117"},{"name":"TRR 318 - Subproject C3","_id":"126"},{"_id":"109","name":"TRR 318: Erklärbarkeit konstruieren"}],"language":[{"iso":"eng"}],"publication":"The Thirteenth International Conference on Learning Representations (ICLR)","type":"conference","status":"public"},{"publication":"Proceedings of the European Symposium on Artificial Neural Networks (ESANN)","type":"conference","status":"public","department":[{"_id":"660"}],"user_id":"93420","_id":"61232","project":[{"name":"TRR 318 - Project Area C","_id":"117"},{"_id":"126","name":"TRR 318 - Subproject C3"},{"name":"TRR 318: Erklärbarkeit konstruieren","_id":"109"},{"_id":"124","name":"TRR 318 ; TP C01: Gesundes Misstrauen in Erklärungen"}],"language":[{"iso":"eng"}],"keyword":["FF"],"citation":{"short":"R. Visser, F. Fumagalli, E. Hüllermeier, B. Hammer, in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN), 2025.","mla":"Visser, Roel, et al. “Explaining Outliers Using Isolation Forest and Shapley Interactions.” <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>, 2025.","bibtex":"@inproceedings{Visser_Fumagalli_Hüllermeier_Hammer_2025, title={Explaining Outliers using Isolation Forest and Shapley Interactions}, booktitle={Proceedings of the European Symposium on Artificial Neural Networks (ESANN)}, author={Visser, Roel and Fumagalli, Fabian and Hüllermeier, Eyke and Hammer, Barbara}, year={2025} }","apa":"Visser, R., Fumagalli, F., Hüllermeier, E., &#38; Hammer, B. (2025). Explaining Outliers using Isolation Forest and Shapley Interactions. <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>.","ieee":"R. Visser, F. Fumagalli, E. Hüllermeier, and B. Hammer, “Explaining Outliers using Isolation Forest and Shapley Interactions,” 2025.","chicago":"Visser, Roel, Fabian Fumagalli, Eyke Hüllermeier, and Barbara Hammer. “Explaining Outliers Using Isolation Forest and Shapley Interactions.” In <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>, 2025.","ama":"Visser R, Fumagalli F, Hüllermeier E, Hammer B. Explaining Outliers using Isolation Forest and Shapley Interactions. In: <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>. ; 2025."},"year":"2025","author":[{"last_name":"Visser","full_name":"Visser, Roel","first_name":"Roel"},{"last_name":"Fumagalli","full_name":"Fumagalli, Fabian","first_name":"Fabian"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"},{"last_name":"Hammer","full_name":"Hammer, Barbara","first_name":"Barbara"}],"date_created":"2025-09-11T15:53:02Z","date_updated":"2025-09-11T15:56:22Z","title":"Explaining Outliers using Isolation Forest and Shapley Interactions"},{"title":"Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory","volume":258,"date_created":"2025-09-11T15:48:55Z","author":[{"first_name":"Fabian","full_name":"Fumagalli, Fabian","last_name":"Fumagalli"},{"full_name":"Muschalik, Maximilian","last_name":"Muschalik","first_name":"Maximilian"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"},{"full_name":"Hammer, Barbara","last_name":"Hammer","first_name":"Barbara"},{"first_name":"Julia","full_name":"Herbinger, Julia","last_name":"Herbinger"}],"date_updated":"2025-09-11T16:24:33Z","publisher":"PMLR","intvolume":"       258","page":"5140-5148","citation":{"bibtex":"@inproceedings{Fumagalli_Muschalik_Hüllermeier_Hammer_Herbinger_2025, series={Proceedings of Machine Learning Research}, title={Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory}, volume={258}, booktitle={Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)}, publisher={PMLR}, author={Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara and Herbinger, Julia}, year={2025}, pages={5140–5148}, collection={Proceedings of Machine Learning Research} }","mla":"Fumagalli, Fabian, et al. “Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory.” <i>Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, vol. 258, PMLR, 2025, pp. 5140–48.","short":"F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, J. Herbinger, in: Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2025, pp. 5140–5148.","apa":"Fumagalli, F., Muschalik, M., Hüllermeier, E., Hammer, B., &#38; Herbinger, J. (2025). Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory. <i>Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, <i>258</i>, 5140–5148.","ieee":"F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, and J. Herbinger, “Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory,” in <i>Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, 2025, vol. 258, pp. 5140–5148.","chicago":"Fumagalli, Fabian, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer, and Julia Herbinger. “Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory.” In <i>Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, 258:5140–48. Proceedings of Machine Learning Research. PMLR, 2025.","ama":"Fumagalli F, Muschalik M, Hüllermeier E, Hammer B, Herbinger J. Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory. In: <i>Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>. Vol 258. Proceedings of Machine Learning Research. PMLR; 2025:5140-5148."},"year":"2025","language":[{"iso":"eng"}],"department":[{"_id":"660"}],"series_title":"Proceedings of Machine Learning Research","user_id":"93420","_id":"61231","project":[{"name":"TRR 318 - Project Area C","_id":"117"},{"_id":"126","name":"TRR 318 - Subproject C3"},{"_id":"109","name":"TRR 318: Erklärbarkeit konstruieren"}],"status":"public","publication":"Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)","type":"conference"},{"abstract":[{"text":"Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the success of techniques depends on various parameters, such as the task, language model, and context provided. Finding an effective prompt is, therefore, often a trial-and-error process. Most existing approaches to automatic prompting aim to optimize individual techniques instead of compositions of techniques and their dependence on the input. To fill this gap, we propose an adaptive prompting approach that predicts the optimal prompt composition ad-hoc for a given input. We apply our approach to social bias detection, a highly context-dependent task that requires semantic understanding. We evaluate it with three large language models on three datasets, comparing compositions to individual techniques and other baselines. The results underline the importance of finding an effective prompt composition. Our approach robustly ensures high detection performance, and is best in several settings. Moreover, first experiments on other tasks support its generalizability.","lang":"eng"}],"publication":"Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)","language":[{"iso":"eng"}],"year":"2025","title":"Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection","publisher":"Association for Computational Linguistics","date_created":"2025-05-10T12:37:45Z","editor":[{"first_name":"Luis","full_name":"Chiruzzo, Luis","last_name":"Chiruzzo"},{"full_name":"Ritter, Alan","last_name":"Ritter","first_name":"Alan"},{"first_name":"Lu","full_name":"Wang, Lu","last_name":"Wang"}],"status":"public","type":"conference","_id":"59856","project":[{"_id":"118","name":"TRR 318: Project Area INF"},{"_id":"126","name":"TRR 318 - Subproject C3"}],"department":[{"_id":"660"}],"user_id":"84035","place":"Albuquerque, New Mexico","page":"2421–2449","citation":{"ama":"Spliethöver M, Knebler T, Fumagalli F, et al. Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection. In: Chiruzzo L, Ritter A, Wang L, eds. <i>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</i>. Association for Computational Linguistics; 2025:2421–2449.","chicago":"Spliethöver, Maximilian, Tim Knebler, Fabian Fumagalli, Maximilian Muschalik, Barbara Hammer, Eyke Hüllermeier, and Henning Wachsmuth. “Adaptive Prompting: Ad-Hoc Prompt Composition for Social Bias Detection.” In <i>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</i>, edited by Luis Chiruzzo, Alan Ritter, and Lu Wang, 2421–2449. Albuquerque, New Mexico: Association for Computational Linguistics, 2025.","ieee":"M. Spliethöver <i>et al.</i>, “Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection,” in <i>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</i>, 2025, pp. 2421–2449.","short":"M. Spliethöver, T. Knebler, F. Fumagalli, M. Muschalik, B. Hammer, E. Hüllermeier, H. Wachsmuth, in: L. Chiruzzo, A. Ritter, L. Wang (Eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Association for Computational Linguistics, Albuquerque, New Mexico, 2025, pp. 2421–2449.","mla":"Spliethöver, Maximilian, et al. “Adaptive Prompting: Ad-Hoc Prompt Composition for Social Bias Detection.” <i>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</i>, edited by Luis Chiruzzo et al., Association for Computational Linguistics, 2025, pp. 2421–2449.","bibtex":"@inproceedings{Spliethöver_Knebler_Fumagalli_Muschalik_Hammer_Hüllermeier_Wachsmuth_2025, place={Albuquerque, New Mexico}, title={Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection}, booktitle={Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)}, publisher={Association for Computational Linguistics}, author={Spliethöver, Maximilian and Knebler, Tim and Fumagalli, Fabian and Muschalik, Maximilian and Hammer, Barbara and Hüllermeier, Eyke and Wachsmuth, Henning}, editor={Chiruzzo, Luis and Ritter, Alan and Wang, Lu}, year={2025}, pages={2421–2449} }","apa":"Spliethöver, M., Knebler, T., Fumagalli, F., Muschalik, M., Hammer, B., Hüllermeier, E., &#38; Wachsmuth, H. (2025). Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection. In L. Chiruzzo, A. Ritter, &#38; L. Wang (Eds.), <i>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</i> (pp. 2421–2449). Association for Computational Linguistics."},"publication_identifier":{"isbn":["979-8-89176-189-6"]},"publication_status":"published","related_material":{"link":[{"url":"https://github.com/webis-de/naacl25-prompt-compositions","relation":"software"}]},"main_file_link":[{"open_access":"1","url":"https://aclanthology.org/2025.naacl-long.122/"}],"date_updated":"2025-09-12T09:51:30Z","oa":"1","author":[{"first_name":"Maximilian","id":"84035","full_name":"Spliethöver, Maximilian","orcid":"0000-0003-4364-1409","last_name":"Spliethöver"},{"full_name":"Knebler, Tim","last_name":"Knebler","first_name":"Tim"},{"full_name":"Fumagalli, Fabian","id":"93420","last_name":"Fumagalli","first_name":"Fabian"},{"first_name":"Maximilian","full_name":"Muschalik, Maximilian","last_name":"Muschalik"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"},{"full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier","first_name":"Eyke"},{"id":"3900","full_name":"Wachsmuth, Henning","last_name":"Wachsmuth","first_name":"Henning"}]},{"page":"1-8","citation":{"chicago":"Kenneweg, Philip, Tristan Kenneweg, Fabian Fumagalli, and Barbara Hammer. “No Learning Rates Needed: Introducing SALSA - Stable Armijo Line Search Adaptation.” In <i>2024 International Joint Conference on Neural Networks (IJCNN)</i>, 1–8, 2024. <a href=\"https://doi.org/10.1109/IJCNN60899.2024.10650124\">https://doi.org/10.1109/IJCNN60899.2024.10650124</a>.","ieee":"P. Kenneweg, T. Kenneweg, F. Fumagalli, and B. Hammer, “No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation,” in <i>2024 International Joint Conference on Neural Networks (IJCNN)</i>, 2024, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/IJCNN60899.2024.10650124\">10.1109/IJCNN60899.2024.10650124</a>.","ama":"Kenneweg P, Kenneweg T, Fumagalli F, Hammer B. No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation. In: <i>2024 International Joint Conference on Neural Networks (IJCNN)</i>. ; 2024:1-8. doi:<a href=\"https://doi.org/10.1109/IJCNN60899.2024.10650124\">10.1109/IJCNN60899.2024.10650124</a>","apa":"Kenneweg, P., Kenneweg, T., Fumagalli, F., &#38; Hammer, B. (2024). No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation. <i>2024 International Joint Conference on Neural Networks (IJCNN)</i>, 1–8. <a href=\"https://doi.org/10.1109/IJCNN60899.2024.10650124\">https://doi.org/10.1109/IJCNN60899.2024.10650124</a>","short":"P. Kenneweg, T. Kenneweg, F. Fumagalli, B. Hammer, in: 2024 International Joint Conference on Neural Networks (IJCNN), 2024, pp. 1–8.","mla":"Kenneweg, Philip, et al. “No Learning Rates Needed: Introducing SALSA - Stable Armijo Line Search Adaptation.” <i>2024 International Joint Conference on Neural Networks (IJCNN)</i>, 2024, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/IJCNN60899.2024.10650124\">10.1109/IJCNN60899.2024.10650124</a>.","bibtex":"@inproceedings{Kenneweg_Kenneweg_Fumagalli_Hammer_2024, title={No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation}, DOI={<a href=\"https://doi.org/10.1109/IJCNN60899.2024.10650124\">10.1109/IJCNN60899.2024.10650124</a>}, booktitle={2024 International Joint Conference on Neural Networks (IJCNN)}, author={Kenneweg, Philip and Kenneweg, Tristan and Fumagalli, Fabian and Hammer, Barbara}, year={2024}, pages={1–8} }"},"year":"2024","date_created":"2025-01-16T16:21:28Z","author":[{"full_name":"Kenneweg, Philip","last_name":"Kenneweg","first_name":"Philip"},{"first_name":"Tristan","last_name":"Kenneweg","full_name":"Kenneweg, Tristan"},{"full_name":"Fumagalli, Fabian","last_name":"Fumagalli","first_name":"Fabian"},{"last_name":"Hammer","full_name":"Hammer, Barbara","first_name":"Barbara"}],"date_updated":"2025-09-11T15:37:42Z","doi":"10.1109/IJCNN60899.2024.10650124","title":"No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation","publication":"2024 International Joint Conference on Neural Networks (IJCNN)","type":"conference","status":"public","department":[{"_id":"660"}],"user_id":"93420","_id":"58224","project":[{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"},{"name":"TRR 318 - C: TRR 318 - Project Area C","_id":"117"},{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"}],"language":[{"iso":"eng"}],"keyword":["Training","Schedules","Codes","Search methods","Source coding","Computer architecture","Transformers"]},{"user_id":"93420","department":[{"_id":"660"}],"project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"}],"_id":"53073","language":[{"iso":"eng"}],"keyword":["Explainable Artificial Intelligence"],"type":"conference","publication":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)","status":"public","abstract":[{"lang":"eng","text":"While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models. As a remedy, the Shapley value (SV) is a well-known concept in explainable artificial intelligence (XAI) research for quantifying additive feature attributions of predictions. The model-specific TreeSHAP methodology solves the exponential complexity for retrieving exact SVs from tree-based models. Expanding beyond individual feature attribution, Shapley interactions reveal the impact of intricate feature interactions of any order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ is supported by a mathematical framework that exploits polynomial arithmetic to compute the interaction scores in a single recursive traversal of the tree, akin to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and explore interactions on well-established benchmark datasets."}],"author":[{"first_name":"Maximilian","last_name":"Muschalik","full_name":"Muschalik, Maximilian"},{"last_name":"Fumagalli","id":"93420","full_name":"Fumagalli, Fabian","first_name":"Fabian"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"},{"first_name":"Eyke","last_name":"Huellermeier","full_name":"Huellermeier, Eyke","id":"48129"}],"date_created":"2024-03-27T14:50:04Z","volume":38,"date_updated":"2025-09-11T16:20:11Z","doi":"10.1609/aaai.v38i13.29352","title":"Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles","issue":"13","publication_status":"published","publication_identifier":{"issn":["2374-3468","2159-5399"]},"citation":{"chicago":"Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier. “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.” In <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, 38:14388–96, 2024. <a href=\"https://doi.org/10.1609/aaai.v38i13.29352\">https://doi.org/10.1609/aaai.v38i13.29352</a>.","ieee":"M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles,” in <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, 2024, vol. 38, no. 13, pp. 14388–14396, doi: <a href=\"https://doi.org/10.1609/aaai.v38i13.29352\">10.1609/aaai.v38i13.29352</a>.","ama":"Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. In: <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>. Vol 38. ; 2024:14388-14396. doi:<a href=\"https://doi.org/10.1609/aaai.v38i13.29352\">10.1609/aaai.v38i13.29352</a>","apa":"Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, <i>38</i>(13), 14388–14396. <a href=\"https://doi.org/10.1609/aaai.v38i13.29352\">https://doi.org/10.1609/aaai.v38i13.29352</a>","mla":"Muschalik, Maximilian, et al. “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.” <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, vol. 38, no. 13, 2024, pp. 14388–96, doi:<a href=\"https://doi.org/10.1609/aaai.v38i13.29352\">10.1609/aaai.v38i13.29352</a>.","short":"M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 14388–14396.","bibtex":"@inproceedings{Muschalik_Fumagalli_Hammer_Huellermeier_2024, title={Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles}, volume={38}, DOI={<a href=\"https://doi.org/10.1609/aaai.v38i13.29352\">10.1609/aaai.v38i13.29352</a>}, number={13}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}, year={2024}, pages={14388–14396} }"},"page":"14388-14396","intvolume":"        38","year":"2024"},{"citation":{"apa":"Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, <i>238</i>, 3520–3528.","mla":"Kolpaczki, Patrick, et al. “SVARM-IQ: Efficient Approximation of Any-Order Shapley Interactions through Stratification.” <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, vol. 238, PMLR, 2024, pp. 3520–3528.","short":"P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2024, pp. 3520–3528.","bibtex":"@inproceedings{Kolpaczki_Muschalik_Fumagalli_Hammer_Huellermeier_2024, series={Proceedings of Machine Learning Research}, title={SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification}, volume={238}, booktitle={Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)}, publisher={PMLR}, author={Kolpaczki, Patrick and Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}, year={2024}, pages={3520–3528}, collection={Proceedings of Machine Learning Research} }","ama":"Kolpaczki P, Muschalik M, Fumagalli F, Hammer B, Huellermeier E. SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. In: <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>. Vol 238. Proceedings of Machine Learning Research. PMLR; 2024:3520–3528.","chicago":"Kolpaczki, Patrick, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier. “SVARM-IQ: Efficient Approximation of Any-Order Shapley Interactions through Stratification.” In <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, 238:3520–3528. Proceedings of Machine Learning Research. PMLR, 2024.","ieee":"P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification,” in <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)</i>, 2024, vol. 238, pp. 3520–3528."},"page":"3520–3528","intvolume":"       238","year":"2024","date_created":"2024-07-18T09:39:14Z","author":[{"first_name":"Patrick","full_name":"Kolpaczki, Patrick","last_name":"Kolpaczki"},{"full_name":"Muschalik, Maximilian","last_name":"Muschalik","first_name":"Maximilian"},{"first_name":"Fabian","id":"93420","full_name":"Fumagalli, Fabian","last_name":"Fumagalli"},{"last_name":"Hammer","full_name":"Hammer, Barbara","first_name":"Barbara"},{"last_name":"Huellermeier","full_name":"Huellermeier, Eyke","id":"48129","first_name":"Eyke"}],"volume":238,"date_updated":"2025-09-11T16:22:30Z","publisher":"PMLR","title":"SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification","type":"conference","publication":"Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS)","status":"public","abstract":[{"lang":"eng","text":"Addressing the limitations of individual attribution scores via the Shapley value (SV), the field of explainable AI (XAI) has recently explored intricate interactions of features or data points. In particular, extensions of the SV, such as the Shapley Interaction Index (SII), have been proposed as a measure to still benefit from the axiomatic basis of the SV. However, similar to the SV, their exact computation remains computationally prohibitive. Hence, we propose with SVARM-IQ a sampling-based approach to efficiently approximate Shapley-based interaction indices of any order. SVARM-IQ can be applied to a broad class of interaction indices, including the SII, by leveraging a novel stratified representation. We provide non-asymptotic theoretical guarantees on its approximation quality and empirically demonstrate that SVARM-IQ achieves state-of-the-art estimation results in practical XAI scenarios on different model classes and application domains."}],"series_title":"Proceedings of Machine Learning Research","user_id":"93420","department":[{"_id":"660"}],"project":[{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"},{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"}],"_id":"55311","language":[{"iso":"eng"}]},{"volume":235,"author":[{"first_name":"Fabian","last_name":"Fumagalli","full_name":"Fumagalli, Fabian"},{"full_name":"Muschalik, Maximilian","last_name":"Muschalik","first_name":"Maximilian"},{"last_name":"Kolpaczki","full_name":"Kolpaczki, Patrick","first_name":"Patrick"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"}],"date_created":"2025-01-16T16:12:16Z","publisher":"PMLR","date_updated":"2025-09-11T16:27:05Z","title":"KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions","intvolume":"       235","page":"14308–14342","citation":{"ieee":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer, “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions,” in <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, 2024, vol. 235, pp. 14308–14342.","chicago":"Fumagalli, Fabian, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, and Barbara Hammer. “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.” In <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, 235:14308–14342. Proceedings of Machine Learning Research. PMLR, 2024.","ama":"Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. In: <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>. Vol 235. Proceedings of Machine Learning Research. PMLR; 2024:14308–14342.","apa":"Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., &#38; Hammer, B. (2024). KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, <i>235</i>, 14308–14342.","short":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in: Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR, 2024, pp. 14308–14342.","bibtex":"@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2024, series={Proceedings of Machine Learning Research}, title={KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}, volume={235}, booktitle={Proceedings of the 41st International Conference on Machine Learning (ICML)}, publisher={PMLR}, author={Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}, year={2024}, pages={14308–14342}, collection={Proceedings of Machine Learning Research} }","mla":"Fumagalli, Fabian, et al. “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.” <i>Proceedings of the 41st International Conference on Machine Learning (ICML)</i>, vol. 235, PMLR, 2024, pp. 14308–14342."},"year":"2024","department":[{"_id":"660"}],"series_title":"Proceedings of Machine Learning Research","user_id":"93420","_id":"58223","project":[{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"},{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"}],"language":[{"iso":"eng"}],"publication":"Proceedings of the 41st International Conference on Machine Learning (ICML)","type":"conference","status":"public","abstract":[{"text":"The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shapley Interaction Index (SII) is a direct axiomatic extension of the SV. While it is well-known that the SV yields an optimal approximation of any game via a weighted least square (WLS) objective, an extension of this result to SII has been a long-standing open problem, which even led to the proposal of an alternative index. In this work, we characterize higher-order SII as a solution to a WLS problem, which constructs an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation for the SV and pairwise SII and give empirically validated conjectures for higher orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, and demonstrate state-of-the-art performance for feature interactions.","lang":"eng"}]},{"year":"2024","citation":{"apa":"Muschalik, M., Baniecki, H., Fumagalli, F., Kolpaczki, P., Hammer, B., &#38; Huellermeier, E. (2024). shapiq: Shapley interactions for machine learning. <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, <i>37</i>, 130324–130357.","bibtex":"@inproceedings{Muschalik_Baniecki_Fumagalli_Kolpaczki_Hammer_Huellermeier_2024, title={shapiq: Shapley interactions for machine learning}, volume={37}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, author={Muschalik, Maximilian and Baniecki, Hubert and Fumagalli, Fabian and Kolpaczki, Patrick and Hammer, Barbara and Huellermeier, Eyke}, year={2024}, pages={130324–130357} }","short":"M. Muschalik, H. Baniecki, F. Fumagalli, P. Kolpaczki, B. Hammer, E. Huellermeier, in: Advances in Neural Information Processing Systems (NeurIPS), 2024, pp. 130324–130357.","mla":"Muschalik, Maximilian, et al. “Shapiq: Shapley Interactions for Machine Learning.” <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, vol. 37, 2024, pp. 130324–130357.","chicago":"Muschalik, Maximilian, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, and Eyke Huellermeier. “Shapiq: Shapley Interactions for Machine Learning.” In <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, 37:130324–130357, 2024.","ieee":"M. Muschalik, H. Baniecki, F. Fumagalli, P. Kolpaczki, B. Hammer, and E. Huellermeier, “shapiq: Shapley interactions for machine learning,” in <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, 2024, vol. 37, pp. 130324–130357.","ama":"Muschalik M, Baniecki H, Fumagalli F, Kolpaczki P, Hammer B, Huellermeier E. shapiq: Shapley interactions for machine learning. In: <i>Advances in Neural Information Processing Systems (NeurIPS)</i>. Vol 37. ; 2024:130324–130357."},"page":"130324–130357","intvolume":"        37","title":"shapiq: Shapley interactions for machine learning","date_updated":"2025-09-11T16:17:35Z","date_created":"2025-09-11T15:39:01Z","author":[{"first_name":"Maximilian","last_name":"Muschalik","full_name":"Muschalik, Maximilian"},{"first_name":"Hubert","last_name":"Baniecki","full_name":"Baniecki, Hubert"},{"id":"93420","full_name":"Fumagalli, Fabian","last_name":"Fumagalli","first_name":"Fabian"},{"first_name":"Patrick","full_name":"Kolpaczki, Patrick","last_name":"Kolpaczki"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"},{"id":"48129","full_name":"Huellermeier, Eyke","last_name":"Huellermeier","first_name":"Eyke"}],"volume":37,"status":"public","type":"conference","publication":"Advances in Neural Information Processing Systems (NeurIPS)","language":[{"iso":"eng"}],"project":[{"name":"TRR 318 - Project Area C","_id":"117"},{"name":"TRR 318 - Subproject C3","_id":"126"},{"_id":"109","name":"TRR 318: Erklärbarkeit konstruieren"}],"_id":"61228","user_id":"93420","department":[{"_id":"660"}]},{"intvolume":"        38","page":"13246–13255","citation":{"apa":"Kolpaczki, P., Bengs, V., Muschalik, M., &#38; Hüllermeier, E. (2024). Approximating the shapley value without marginal contributions. <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, <i>38</i>(12), 13246–13255.","short":"P. Kolpaczki, V. Bengs, M. Muschalik, E. Hüllermeier, in: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 13246–13255.","mla":"Kolpaczki, Patrick, et al. “Approximating the Shapley Value without Marginal Contributions.” <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, vol. 38, no. 12, 2024, pp. 13246–13255.","bibtex":"@inproceedings{Kolpaczki_Bengs_Muschalik_Hüllermeier_2024, title={Approximating the shapley value without marginal contributions}, volume={38}, number={12}, booktitle={Proceedings of the AAAI conference on Artificial Intelligence (AAAI)}, author={Kolpaczki, Patrick and Bengs, Viktor and Muschalik, Maximilian and Hüllermeier, Eyke}, year={2024}, pages={13246–13255} }","chicago":"Kolpaczki, Patrick, Viktor Bengs, Maximilian Muschalik, and Eyke Hüllermeier. “Approximating the Shapley Value without Marginal Contributions.” In <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>, 38:13246–13255, 2024.","ieee":"P. Kolpaczki, V. Bengs, M. Muschalik, and E. Hüllermeier, “Approximating the shapley value without marginal contributions,” in <i>Proceedings of the AAAI conference on Artificial Intelligence (AAAI)</i>, 2024, vol. 38, no. 12, pp. 13246–13255.","ama":"Kolpaczki P, Bengs V, Muschalik M, Hüllermeier E. Approximating the shapley value without marginal contributions. In: <i>Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</i>. Vol 38. ; 2024:13246–13255."},"year":"2024","issue":"12","title":"Approximating the shapley value without marginal contributions","volume":38,"date_created":"2025-09-11T15:46:40Z","author":[{"last_name":"Kolpaczki","full_name":"Kolpaczki, Patrick","first_name":"Patrick"},{"full_name":"Bengs, Viktor","last_name":"Bengs","first_name":"Viktor"},{"last_name":"Muschalik","full_name":"Muschalik, Maximilian","first_name":"Maximilian"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_updated":"2025-09-11T16:17:54Z","status":"public","publication":"Proceedings of the AAAI conference on Artificial Intelligence (AAAI)","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"660"}],"user_id":"93420","_id":"61230","project":[{"_id":"117","name":"TRR 318 - Project Area C"},{"_id":"126","name":"TRR 318 - Subproject C3"},{"name":"TRR 318: Erklärbarkeit konstruieren","_id":"109"}]},{"user_id":"93420","department":[{"_id":"660"}],"project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"name":"TRR 318 - C: TRR 318 - Project Area C","_id":"117"},{"grant_number":"438445824","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"}],"_id":"50262","type":"journal_article","status":"public","author":[{"first_name":"Fabian","full_name":"Fumagalli, Fabian","last_name":"Fumagalli"},{"first_name":"Maximilian","last_name":"Muschalik","full_name":"Muschalik, Maximilian"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"}],"volume":112,"date_updated":"2025-01-16T16:20:12Z","doi":"10.1007/s10994-023-06385-y","publication_status":"published","publication_identifier":{"issn":["0885-6125","1573-0565"]},"citation":{"ama":"Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. Incremental permutation feature importance (iPFI): towards online explanations on data streams. <i>Machine Learning</i>. 2023;112(12):4863-4903. doi:<a href=\"https://doi.org/10.1007/s10994-023-06385-y\">10.1007/s10994-023-06385-y</a>","ieee":"F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “Incremental permutation feature importance (iPFI): towards online explanations on data streams,” <i>Machine Learning</i>, vol. 112, no. 12, pp. 4863–4903, 2023, doi: <a href=\"https://doi.org/10.1007/s10994-023-06385-y\">10.1007/s10994-023-06385-y</a>.","chicago":"Fumagalli, Fabian, Maximilian Muschalik, Eyke Hüllermeier, and Barbara Hammer. “Incremental Permutation Feature Importance (IPFI): Towards Online Explanations on Data Streams.” <i>Machine Learning</i> 112, no. 12 (2023): 4863–4903. <a href=\"https://doi.org/10.1007/s10994-023-06385-y\">https://doi.org/10.1007/s10994-023-06385-y</a>.","short":"F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, Machine Learning 112 (2023) 4863–4903.","mla":"Fumagalli, Fabian, et al. “Incremental Permutation Feature Importance (IPFI): Towards Online Explanations on Data Streams.” <i>Machine Learning</i>, vol. 112, no. 12, Springer Science and Business Media LLC, 2023, pp. 4863–903, doi:<a href=\"https://doi.org/10.1007/s10994-023-06385-y\">10.1007/s10994-023-06385-y</a>.","bibtex":"@article{Fumagalli_Muschalik_Hüllermeier_Hammer_2023, title={Incremental permutation feature importance (iPFI): towards online explanations on data streams}, volume={112}, DOI={<a href=\"https://doi.org/10.1007/s10994-023-06385-y\">10.1007/s10994-023-06385-y</a>}, number={12}, journal={Machine Learning}, publisher={Springer Science and Business Media LLC}, author={Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara}, year={2023}, pages={4863–4903} }","apa":"Fumagalli, F., Muschalik, M., Hüllermeier, E., &#38; Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. <i>Machine Learning</i>, <i>112</i>(12), 4863–4903. <a href=\"https://doi.org/10.1007/s10994-023-06385-y\">https://doi.org/10.1007/s10994-023-06385-y</a>"},"page":"4863-4903","intvolume":"       112","language":[{"iso":"eng"}],"keyword":["Artificial Intelligence","Software"],"publication":"Machine Learning","abstract":[{"text":"<jats:title>Abstract</jats:title><jats:p>Explainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.</jats:p>","lang":"eng"}],"date_created":"2024-01-05T21:52:28Z","publisher":"Springer Science and Business Media LLC","title":"Incremental permutation feature importance (iPFI): towards online explanations on data streams","issue":"12","year":"2023"},{"publication":"Proceedings of the World Conference on Explainable Artificial Intelligence (xAI)","type":"conference","status":"public","_id":"48778","project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"}],"department":[{"_id":"660"}],"user_id":"93420","language":[{"iso":"eng"}],"publication_identifier":{"eisbn":["1865-0937"],"isbn":["9783031440632"],"eissn":["9783031440649"],"issn":["1865-0929"]},"publication_status":"published","year":"2023","citation":{"ama":"Muschalik M, Fumagalli F, Jagtani R, Hammer B, Huellermeier E. iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In: <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>. ; 2023. doi:<a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, and Eyke Huellermeier. “IPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.” In <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>, 2023. <a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">https://doi.org/10.1007/978-3-031-44064-9_11</a>.","ieee":"M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, and E. Huellermeier, “iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios,” 2023, doi: <a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>.","apa":"Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., &#38; Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>. <a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">https://doi.org/10.1007/978-3-031-44064-9_11</a>","bibtex":"@inproceedings{Muschalik_Fumagalli_Jagtani_Hammer_Huellermeier_2023, title={iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>}, booktitle={Proceedings of the World Conference on Explainable Artificial Intelligence (xAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Jagtani, Rohit and Hammer, Barbara and Huellermeier, Eyke}, year={2023} }","mla":"Muschalik, Maximilian, et al. “IPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.” <i>Proceedings of the World Conference on Explainable Artificial Intelligence (XAI)</i>, 2023, doi:<a href=\"https://doi.org/10.1007/978-3-031-44064-9_11\">10.1007/978-3-031-44064-9_11</a>.","short":"M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, E. Huellermeier, in: Proceedings of the World Conference on Explainable Artificial Intelligence (XAI), 2023."},"date_updated":"2025-09-11T16:14:34Z","date_created":"2023-11-10T14:17:17Z","author":[{"first_name":"Maximilian","last_name":"Muschalik","full_name":"Muschalik, Maximilian"},{"first_name":"Fabian","last_name":"Fumagalli","full_name":"Fumagalli, Fabian","id":"93420"},{"first_name":"Rohit","last_name":"Jagtani","full_name":"Jagtani, Rohit"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"},{"first_name":"Eyke","last_name":"Huellermeier","full_name":"Huellermeier, Eyke","id":"48129"}],"title":"iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios","doi":"10.1007/978-3-031-44064-9_11"},{"date_updated":"2025-09-11T16:27:26Z","publisher":"Springer Nature Switzerland","date_created":"2023-11-10T14:11:20Z","author":[{"first_name":"Maximilian","full_name":"Muschalik, Maximilian","last_name":"Muschalik"},{"last_name":"Fumagalli","full_name":"Fumagalli, Fabian","id":"93420","first_name":"Fabian"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"},{"full_name":"Huellermeier, Eyke","id":"48129","last_name":"Huellermeier","first_name":"Eyke"}],"title":"iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams","doi":"10.1007/978-3-031-43418-1_26","publication_identifier":{"eisbn":["9783031434181"],"eissn":["1611-3349"],"isbn":["9783031434174"],"issn":["0302-9743"]},"publication_status":"published","year":"2023","citation":{"ieee":"M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams,” in <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>, Springer Nature Switzerland, 2023.","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier. “ISAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.” In <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>. Springer Nature Switzerland, 2023. <a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">https://doi.org/10.1007/978-3-031-43418-1_26</a>.","ama":"Muschalik M, Fumagalli F, Hammer B, Huellermeier E. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In: <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>. Springer Nature Switzerland; 2023. doi:<a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">10.1007/978-3-031-43418-1_26</a>","short":"M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD), Springer Nature Switzerland, 2023.","mla":"Muschalik, Maximilian, et al. “ISAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.” <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>, Springer Nature Switzerland, 2023, doi:<a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">10.1007/978-3-031-43418-1_26</a>.","bibtex":"@inbook{Muschalik_Fumagalli_Hammer_Huellermeier_2023, title={iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">10.1007/978-3-031-43418-1_26</a>}, booktitle={Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)}, publisher={Springer Nature Switzerland}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}, year={2023} }","apa":"Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2023). iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In <i>Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)</i>. Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-031-43418-1_26\">https://doi.org/10.1007/978-3-031-43418-1_26</a>"},"_id":"48776","project":[{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"},{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"}],"department":[{"_id":"660"}],"user_id":"93420","language":[{"iso":"eng"}],"publication":"Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)","type":"book_chapter","status":"public"},{"author":[{"last_name":"Fumagalli","id":"93420","full_name":"Fumagalli, Fabian","first_name":"Fabian"},{"first_name":"Maximilian","full_name":"Muschalik, Maximilian","last_name":"Muschalik"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"}],"date_created":"2023-11-10T14:00:08Z","date_updated":"2025-09-11T16:26:21Z","doi":"10.14428/ESANN/2023.ES2023-148","conference":{"name":"ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning","location":"Bruges (Belgium) and online"},"title":"On Feature Removal for Explainability in Dynamic Environments","publication_identifier":{"unknown":[" 978-2-87587-088-9"]},"publication_status":"published","citation":{"ama":"Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. On Feature Removal for Explainability in Dynamic Environments. In: <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>. ; 2023. doi:<a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>","chicago":"Fumagalli, Fabian, Maximilian Muschalik, Eyke Hüllermeier, and Barbara Hammer. “On Feature Removal for Explainability in Dynamic Environments.” In <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>, 2023. <a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">https://doi.org/10.14428/ESANN/2023.ES2023-148</a>.","ieee":"F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “On Feature Removal for Explainability in Dynamic Environments,” presented at the ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online, 2023, doi: <a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>.","apa":"Fumagalli, F., Muschalik, M., Hüllermeier, E., &#38; Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. <a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">https://doi.org/10.14428/ESANN/2023.ES2023-148</a>","short":"F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN), 2023.","bibtex":"@inproceedings{Fumagalli_Muschalik_Hüllermeier_Hammer_2023, title={On Feature Removal for Explainability in Dynamic Environments}, DOI={<a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>}, booktitle={Proceedings of the European Symposium on Artificial Neural Networks (ESANN)}, author={Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara}, year={2023} }","mla":"Fumagalli, Fabian, et al. “On Feature Removal for Explainability in Dynamic Environments.” <i>Proceedings of the European Symposium on Artificial Neural Networks (ESANN)</i>, 2023, doi:<a href=\"https://doi.org/10.14428/ESANN/2023.ES2023-148\">10.14428/ESANN/2023.ES2023-148</a>."},"year":"2023","department":[{"_id":"660"}],"user_id":"93420","_id":"48775","project":[{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"},{"name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"}],"language":[{"iso":"eng"}],"publication":"Proceedings of the European Symposium on Artificial Neural Networks (ESANN)","type":"conference","status":"public"},{"year":"2023","citation":{"ama":"Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. SHAP-IQ: Unified Approximation of any-order Shapley Interactions. In: <i>Advances in Neural Information Processing Systems (NeurIPS)</i>. Vol 36. ; 2023:11515--11551.","ieee":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer, “SHAP-IQ: Unified Approximation of any-order Shapley Interactions,” in <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, 2023, vol. 36, pp. 11515--11551.","chicago":"Fumagalli, Fabian, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, and Barbara Hammer. “SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions.” In <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, 36:11515--11551, 2023.","apa":"Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., &#38; Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, <i>36</i>, 11515--11551.","bibtex":"@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2023, title={SHAP-IQ: Unified Approximation of any-order Shapley Interactions}, volume={36}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, author={Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}, year={2023}, pages={11515--11551} }","mla":"Fumagalli, Fabian, et al. “SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions.” <i>Advances in Neural Information Processing Systems (NeurIPS)</i>, vol. 36, 2023, pp. 11515--11551.","short":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in: Advances in Neural Information Processing Systems (NeurIPS), 2023, pp. 11515--11551."},"page":"11515--11551","intvolume":"        36","date_updated":"2025-09-11T16:18:16Z","author":[{"id":"93420","full_name":"Fumagalli, Fabian","last_name":"Fumagalli","first_name":"Fabian"},{"full_name":"Muschalik, Maximilian","last_name":"Muschalik","first_name":"Maximilian"},{"last_name":"Kolpaczki","full_name":"Kolpaczki, Patrick","first_name":"Patrick"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"},{"full_name":"Hammer, Barbara","last_name":"Hammer","first_name":"Barbara"}],"date_created":"2024-03-01T14:15:31Z","volume":36,"title":"SHAP-IQ: Unified Approximation of any-order Shapley Interactions","type":"conference","publication":"Advances in Neural Information Processing Systems (NeurIPS)","status":"public","project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"}],"_id":"52230","user_id":"93420","department":[{"_id":"660"}],"language":[{"iso":"eng"}]},{"keyword":["Artificial Intelligence"],"language":[{"iso":"eng"}],"project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"},{"grant_number":"438445824","_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren"}],"_id":"48780","user_id":"93420","department":[{"_id":"660"}],"abstract":[{"lang":"eng","text":"Explainable Artificial Intelligence (XAI) has mainly focused on static learning tasks so far. In this paper, we consider XAI in the context of online learning in dynamic environments, such as learning from real-time data streams, where models are learned incrementally and continuously adapted over the course of time. More specifically, we motivate the problem of explaining model change, i.e. explaining the difference between models before and after adaptation, instead of the models themselves. In this regard, we provide the first efficient model-agnostic approach to dynamically detecting, quantifying, and explaining significant model changes. Our approach is based on an adaptation of the well-known Permutation Feature Importance (PFI) measure. It includes two hyperparameters that control the sensitivity and directly influence explanation frequency, so that a human user can adjust the method to individual requirements and application needs. We assess and validate our method’s efficacy on illustrative synthetic data streams with three popular model classes."}],"status":"public","type":"journal_article","publication":"KI - Künstliche Intelligenz","title":"Agnostic Explanation of Model Change based on Feature Importance","doi":"10.1007/s13218-022-00766-6","date_updated":"2025-01-16T16:19:35Z","publisher":"Springer Science and Business Media LLC","date_created":"2023-11-10T14:21:06Z","author":[{"last_name":"Muschalik","full_name":"Muschalik, Maximilian","first_name":"Maximilian"},{"last_name":"Fumagalli","full_name":"Fumagalli, Fabian","id":"93420","first_name":"Fabian"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"},{"first_name":"Eyke","last_name":"Huellermeier","full_name":"Huellermeier, Eyke","id":"48129"}],"volume":36,"year":"2022","citation":{"apa":"Muschalik, M., Fumagalli, F., Hammer, B., &#38; Huellermeier, E. (2022). Agnostic Explanation of Model Change based on Feature Importance. <i>KI - Künstliche Intelligenz</i>, <i>36</i>(3–4), 211–224. <a href=\"https://doi.org/10.1007/s13218-022-00766-6\">https://doi.org/10.1007/s13218-022-00766-6</a>","short":"M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, KI - Künstliche Intelligenz 36 (2022) 211–224.","bibtex":"@article{Muschalik_Fumagalli_Hammer_Huellermeier_2022, title={Agnostic Explanation of Model Change based on Feature Importance}, volume={36}, DOI={<a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>}, number={3–4}, journal={KI - Künstliche Intelligenz}, publisher={Springer Science and Business Media LLC}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}, year={2022}, pages={211–224} }","mla":"Muschalik, Maximilian, et al. “Agnostic Explanation of Model Change Based on Feature Importance.” <i>KI - Künstliche Intelligenz</i>, vol. 36, no. 3–4, Springer Science and Business Media LLC, 2022, pp. 211–24, doi:<a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>.","ieee":"M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Agnostic Explanation of Model Change based on Feature Importance,” <i>KI - Künstliche Intelligenz</i>, vol. 36, no. 3–4, pp. 211–224, 2022, doi: <a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>.","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier. “Agnostic Explanation of Model Change Based on Feature Importance.” <i>KI - Künstliche Intelligenz</i> 36, no. 3–4 (2022): 211–24. <a href=\"https://doi.org/10.1007/s13218-022-00766-6\">https://doi.org/10.1007/s13218-022-00766-6</a>.","ama":"Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Agnostic Explanation of Model Change based on Feature Importance. <i>KI - Künstliche Intelligenz</i>. 2022;36(3-4):211-224. doi:<a href=\"https://doi.org/10.1007/s13218-022-00766-6\">10.1007/s13218-022-00766-6</a>"},"page":"211-224","intvolume":"        36","publication_status":"published","publication_identifier":{"issn":["0933-1875","1610-1987"]},"issue":"3-4"}]