[{"language":[{"iso":"eng"}],"doi":"10.1609/aaai.v38i13.29352","date_updated":"2024-03-27T15:06:39Z","project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"_id":"109","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","grant_number":"438445824"},{"_id":"117","name":"TRR 318 - C: TRR 318 - Project Area C"}],"publication_identifier":{"issn":["2374-3468","2159-5399"]},"publication_status":"published","department":[{"_id":"660"}],"title":"Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles","page":"14388-14396","type":"journal_article","citation":{"short":"M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, Proceedings of the AAAI Conference on Artificial Intelligence 38 (2024) 14388–14396.","ieee":"M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 13, pp. 14388–14396, 2024, doi: 10.1609/aaai.v38i13.29352.","apa":"Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388–14396. https://doi.org/10.1609/aaai.v38i13.29352","ama":"Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(13):14388-14396. doi:10.1609/aaai.v38i13.29352","chicago":"Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier. “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.” Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14388–96. https://doi.org/10.1609/aaai.v38i13.29352.","mla":"Muschalik, Maximilian, et al. “Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 13, Association for the Advancement of Artificial Intelligence (AAAI), 2024, pp. 14388–96, doi:10.1609/aaai.v38i13.29352.","bibtex":"@article{Muschalik_Fumagalli_Hammer_Huellermeier_2024, title={Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles}, volume={38}, DOI={10.1609/aaai.v38i13.29352}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, publisher={Association for the Advancement of Artificial Intelligence (AAAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}, year={2024}, pages={14388–14396} }"},"year":"2024","issue":"13","intvolume":" 38","_id":"53073","date_created":"2024-03-27T14:50:04Z","status":"public","volume":38,"keyword":["Explainable Artificial Intelligence"],"publication":"Proceedings of the AAAI Conference on Artificial Intelligence","author":[{"last_name":"Muschalik","first_name":"Maximilian","full_name":"Muschalik, Maximilian"},{"last_name":"Fumagalli","id":"93420","first_name":"Fabian","full_name":"Fumagalli, Fabian"},{"last_name":"Hammer","first_name":"Barbara","full_name":"Hammer, Barbara"},{"first_name":"Eyke","full_name":"Huellermeier, Eyke","last_name":"Huellermeier","id":"48129"}],"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","user_id":"93420","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."}],"article_type":"original"},{"abstract":[{"lang":"eng","text":"For open world applications, deep neural networks (DNNs) need to be aware of\r\npreviously unseen data and adaptable to evolving environments. Furthermore, it\r\nis desirable to detect and learn novel classes which are not included in the\r\nDNNs underlying set of semantic classes in an unsupervised fashion. The method\r\nproposed in this article builds upon anomaly detection to retrieve\r\nout-of-distribution (OoD) data as candidates for new classes. We thereafter\r\nextend the DNN by $k$ empty classes and fine-tune it on the OoD data samples.\r\nTo this end, we introduce two loss functions, which 1) entice the DNN to assign\r\nOoD samples to the empty classes and 2) to minimize the inner-class feature\r\ndistances between them. Thus, instead of ground truth which contains labels for\r\nthe different novel classes, the DNN obtains a single OoD label together with a\r\ndistance matrix, which is computed in advance. We perform several experiments\r\nfor image classification and semantic segmentation, which demonstrate that a\r\nDNN can extend its own semantic space by multiple classes without having access\r\nto ground truth."}],"external_id":{"arxiv":["2305.00983"]},"title":"Detecting Novelties with Empty Classes","user_id":"44040","author":[{"last_name":"Uhlemeyer","full_name":"Uhlemeyer, Svenja","first_name":"Svenja"},{"last_name":"Lienen","id":"44040","first_name":"Julian","full_name":"Lienen, Julian"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"full_name":"Gottschalk, Hanno","first_name":"Hanno","last_name":"Gottschalk"}],"publication":"arXiv:2305.00983","status":"public","date_created":"2023-05-05T11:37:00Z","date_updated":"2023-05-05T11:39:10Z","_id":"44512","oa":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/2305.00983.pdf"}],"year":"2023","type":"preprint","citation":{"mla":"Uhlemeyer, Svenja, et al. “Detecting Novelties with Empty Classes.” ArXiv:2305.00983, 2023.","bibtex":"@article{Uhlemeyer_Lienen_Hüllermeier_Gottschalk_2023, title={Detecting Novelties with Empty Classes}, journal={arXiv:2305.00983}, author={Uhlemeyer, Svenja and Lienen, Julian and Hüllermeier, Eyke and Gottschalk, Hanno}, year={2023} }","chicago":"Uhlemeyer, Svenja, Julian Lienen, Eyke Hüllermeier, and Hanno Gottschalk. “Detecting Novelties with Empty Classes.” ArXiv:2305.00983, 2023.","apa":"Uhlemeyer, S., Lienen, J., Hüllermeier, E., & Gottschalk, H. (2023). Detecting Novelties with Empty Classes. In arXiv:2305.00983.","ama":"Uhlemeyer S, Lienen J, Hüllermeier E, Gottschalk H. Detecting Novelties with Empty Classes. arXiv:230500983. Published online 2023.","ieee":"S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties with Empty Classes,” arXiv:2305.00983. 2023.","short":"S. Uhlemeyer, J. Lienen, E. Hüllermeier, H. Gottschalk, ArXiv:2305.00983 (2023)."},"language":[{"iso":"eng"}]},{"oa":"1","conference":{"location":"Kigali, Ruanda","name":"International Conference on Learning Representations, ICLR"},"date_updated":"2023-06-29T09:14:26Z","_id":"31880","type":"conference","year":"2023","citation":{"short":"D.A. Nguyen, R. Levie, J. Lienen, G. Kutyniok, E. Hüllermeier, in: International Conference on Learning Representations, ICLR, 2023.","ieee":"D. A. Nguyen, R. Levie, J. Lienen, G. Kutyniok, and E. Hüllermeier, “Memorization-Dilation: Modeling Neural Collapse Under Noise,” presented at the International Conference on Learning Representations, ICLR, Kigali, Ruanda, 2023.","apa":"Nguyen, D. A., Levie, R., Lienen, J., Kutyniok, G., & Hüllermeier, E. (2023). Memorization-Dilation: Modeling Neural Collapse Under Noise. International Conference on Learning Representations, ICLR. International Conference on Learning Representations, ICLR, Kigali, Ruanda.","ama":"Nguyen DA, Levie R, Lienen J, Kutyniok G, Hüllermeier E. Memorization-Dilation: Modeling Neural Collapse Under Noise. In: International Conference on Learning Representations, ICLR. ; 2023.","chicago":"Nguyen, Duc Anh, Ron Levie, Julian Lienen, Gitta Kutyniok, and Eyke Hüllermeier. “Memorization-Dilation: Modeling Neural Collapse Under Noise.” In International Conference on Learning Representations, ICLR, 2023.","mla":"Nguyen, Duc Anh, et al. “Memorization-Dilation: Modeling Neural Collapse Under Noise.” International Conference on Learning Representations, ICLR, 2023.","bibtex":"@inproceedings{Nguyen_Levie_Lienen_Kutyniok_Hüllermeier_2023, title={Memorization-Dilation: Modeling Neural Collapse Under Noise}, booktitle={International Conference on Learning Representations, ICLR}, author={Nguyen, Duc Anh and Levie, Ron and Lienen, Julian and Kutyniok, Gitta and Hüllermeier, Eyke}, year={2023} }"},"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://arxiv.org/abs/2206.05530","open_access":"1"}],"title":"Memorization-Dilation: Modeling Neural Collapse Under Noise","user_id":"44040","abstract":[{"lang":"eng","text":"The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase of training a deep neural network, the feature embedding of all examples of the same class tend to collapse to a single representation, and the features of different classes tend to separate as much as possible. Neural collapse is often studied through a simplified model, called the unconstrained feature representation, in which the model is assumed to have \"infinite expressivity\" and can map each data point to any arbitrary representation. In this work, we propose a more realistic variant of the unconstrained feature representation that takes the limited expressivity of the network into account. Empirical evidence suggests that the memorization of noisy data points leads to a degradation (dilation) of the neural collapse. Using a model of the memorization-dilation (M-D) phenomenon, we show one mechanism by which different losses lead to different performances of the trained network on noisy data. Our proofs reveal why label smoothing, a modification of cross-entropy empirically observed to produce a regularization effect, leads to improved generalization in classification tasks."}],"date_created":"2022-06-14T14:48:36Z","status":"public","publication":"International Conference on Learning Representations, ICLR","author":[{"last_name":"Nguyen","full_name":"Nguyen, Duc Anh","first_name":"Duc Anh"},{"last_name":"Levie","full_name":"Levie, Ron","first_name":"Ron"},{"first_name":"Julian","full_name":"Lienen, Julian","last_name":"Lienen","id":"44040"},{"last_name":"Kutyniok","first_name":"Gitta","full_name":"Kutyniok, Gitta"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}]},{"type":"book_chapter","citation":{"ieee":"J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.","short":"J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A. Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 85–104.","bibtex":"@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Configuration and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466}, booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","mla":"Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:10.5281/zenodo.8068466.","chicago":"Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration and Evaluation.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.5281/zenodo.8068466.","ama":"Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104. doi:10.5281/zenodo.8068466","apa":"Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466"},"year":"2023","page":"85-104","intvolume":" 412","_id":"45884","volume":412,"status":"public","has_accepted_license":"1","date_created":"2023-07-07T07:50:53Z","publisher":"Heinz Nixdorf Institut, Universität Paderborn","author":[{"orcid":"0000-0002-1231-4985","full_name":"Hanselle, Jonas Manuel","first_name":"Jonas Manuel","id":"43980","last_name":"Hanselle"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille","id":"65716","last_name":"Ngonga Ngomo"},{"full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","first_name":"Mohamed","id":"67234","last_name":"Sherif"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","last_name":"Wever","id":"33176"}],"file_date_updated":"2023-07-07T11:20:11Z","publication":"On-The-Fly Computing -- Individualized IT-services in dynamic markets","file":[{"access_level":"open_access","date_created":"2023-07-07T07:50:34Z","file_name":"B2-Chapter-SFB-Buch-Final.pdf","content_type":"application/pdf","date_updated":"2023-07-07T11:20:11Z","relation":"main_file","file_size":895091,"creator":"florida","file_id":"45885"}],"ddc":["040"],"user_id":"477","language":[{"iso":"eng"}],"series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","doi":"10.5281/zenodo.8068466","oa":"1","date_updated":"2023-07-07T11:20:12Z","editor":[{"full_name":"Haake, Claus-Jochen","first_name":"Claus-Jochen","last_name":"Haake"},{"full_name":"Meyer auf der Heide, Friedhelm","first_name":"Friedhelm","last_name":"Meyer auf der Heide"},{"last_name":"Platzner","first_name":"Marco","full_name":"Platzner, Marco"},{"last_name":"Wachsmuth","first_name":"Henning","full_name":"Wachsmuth, Henning"},{"full_name":"Wehrheim, Heike","first_name":"Heike","last_name":"Wehrheim"}],"project":[{"_id":"1","grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten "},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - B2: Konfiguration und Bewertung (B02)","grant_number":"160364472"}],"department":[{"_id":"7"}],"title":"Configuration and Evaluation","place":"Paderborn"},{"date_updated":"2023-07-07T11:19:40Z","doi":"10.5281/zenodo.8068510","oa":"1","series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","language":[{"iso":"eng"}],"place":"Paderborn","title":"Composition Analysis in Unknown Contexts","department":[{"_id":"7"}],"editor":[{"last_name":"Haake","full_name":"Haake, Claus-Jochen","first_name":"Claus-Jochen"},{"last_name":"Meyer auf der Heide","full_name":"Meyer auf der Heide, Friedhelm","first_name":"Friedhelm"},{"first_name":"Marco","full_name":"Platzner, Marco","last_name":"Platzner"},{"first_name":"Henning","full_name":"Wachsmuth, Henning","last_name":"Wachsmuth"},{"first_name":"Heike","full_name":"Wehrheim, Heike","last_name":"Wehrheim"}],"project":[{"grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B3: SFB 901 - Subproject B3","_id":"11"}],"_id":"45886","intvolume":" 412","page":"105-123","type":"book_chapter","citation":{"ieee":"H. Wehrheim, E. Hüllermeier, S. Becker, M. Becker, C. Richter, and A. Sharma, “Composition Analysis in Unknown Contexts,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 105–123.","short":"H. Wehrheim, E. Hüllermeier, S. Becker, M. Becker, C. Richter, A. Sharma, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 105–123.","mla":"Wehrheim, Heike, et al. “Composition Analysis in Unknown Contexts.” On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 105–23, doi:10.5281/zenodo.8068510.","bibtex":"@inbook{Wehrheim_Hüllermeier_Becker_Becker_Richter_Sharma_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Composition Analysis in Unknown Contexts}, volume={412}, DOI={10.5281/zenodo.8068510}, booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Wehrheim, Heike and Hüllermeier, Eyke and Becker, Steffen and Becker, Matthias and Richter, Cedric and Sharma, Arnab}, editor={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, pages={105–123}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","ama":"Wehrheim H, Hüllermeier E, Becker S, Becker M, Richter C, Sharma A. Composition Analysis in Unknown Contexts. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:105-123. doi:10.5281/zenodo.8068510","apa":"Wehrheim, H., Hüllermeier, E., Becker, S., Becker, M., Richter, C., & Sharma, A. (2023). Composition Analysis in Unknown Contexts. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 105–123). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068510","chicago":"Wehrheim, Heike, Eyke Hüllermeier, Steffen Becker, Matthias Becker, Cedric Richter, and Arnab Sharma. “Composition Analysis in Unknown Contexts.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:105–23. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.5281/zenodo.8068510."},"year":"2023","ddc":["004"],"user_id":"477","file_date_updated":"2023-07-07T11:19:40Z","publication":"On-The-Fly Computing -- Individualized IT-services in dynamic markets","publisher":"Heinz Nixdorf Institut, Universität Paderborn","author":[{"first_name":"Heike","full_name":"Wehrheim, Heike","last_name":"Wehrheim","id":"573"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"full_name":"Becker, Steffen","first_name":"Steffen","last_name":"Becker"},{"first_name":"Matthias","full_name":"Becker, Matthias","last_name":"Becker"},{"full_name":"Richter, Cedric","first_name":"Cedric","id":"50003","last_name":"Richter"},{"first_name":"Arnab","full_name":"Sharma, Arnab","last_name":"Sharma","id":"67200"}],"file":[{"file_name":"B3-Chapter-SFB-Buch-Final.pdf","date_created":"2023-07-07T07:55:57Z","access_level":"open_access","file_id":"45887","creator":"florida","file_size":370888,"relation":"main_file","content_type":"application/pdf","date_updated":"2023-07-07T11:19:40Z"}],"volume":412,"date_created":"2023-07-07T07:56:08Z","status":"public","has_accepted_license":"1"},{"abstract":[{"lang":"eng","text":"Label noise poses an important challenge in machine learning, especially in\r\ndeep learning, in which large models with high expressive power dominate the\r\nfield. Models of that kind are prone to memorizing incorrect labels, thereby\r\nharming generalization performance. Many methods have been proposed to address\r\nthis problem, including robust loss functions and more complex label correction\r\napproaches. Robust loss functions are appealing due to their simplicity, but\r\ntypically lack flexibility, while label correction usually adds substantial\r\ncomplexity to the training setup. In this paper, we suggest to address the\r\nshortcomings of both methodologies by \"ambiguating\" the target information,\r\nadding additional, complementary candidate labels in case the learner is not\r\nsufficiently convinced of the observed training label. More precisely, we\r\nleverage the framework of so-called superset learning to construct set-valued\r\ntargets based on a confidence threshold, which deliver imprecise yet more\r\nreliable beliefs about the ground-truth, effectively helping the learner to\r\nsuppress the memorization effect. In an extensive empirical evaluation, our\r\nmethod demonstrates favorable learning behavior on synthetic and real-world\r\nnoise, confirming the effectiveness in detecting and correcting erroneous\r\ntraining labels."}],"external_id":{"arxiv":["2305.13764"]},"user_id":"44040","title":"Mitigating Label Noise through Data Ambiguation","publication":"arXiv:2305.13764","author":[{"id":"44040","last_name":"Lienen","full_name":"Lienen, Julian","first_name":"Julian"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2023-07-09T11:25:48Z","status":"public","date_updated":"2023-07-09T11:26:21Z","_id":"45911","oa":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2305.13764"}],"language":[{"iso":"eng"}],"citation":{"apa":"Lienen, J., & Hüllermeier, E. (2023). Mitigating Label Noise through Data Ambiguation. In arXiv:2305.13764.","ama":"Lienen J, Hüllermeier E. Mitigating Label Noise through Data Ambiguation. arXiv:230513764. Published online 2023.","chicago":"Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data Ambiguation.” ArXiv:2305.13764, 2023.","mla":"Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data Ambiguation.” ArXiv:2305.13764, 2023.","bibtex":"@article{Lienen_Hüllermeier_2023, title={Mitigating Label Noise through Data Ambiguation}, journal={arXiv:2305.13764}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2023} }","short":"J. Lienen, E. Hüllermeier, ArXiv:2305.13764 (2023).","ieee":"J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,” arXiv:2305.13764. 2023."},"type":"preprint","year":"2023"},{"department":[{"_id":"101"},{"_id":"636"},{"_id":"355"},{"_id":"655"}],"publication_status":"published","external_id":{"arxiv":["arXiv:2104.03562"]},"related_material":{"link":[{"url":"https://github.com/lueckem/quadrature-ML","description":"GitHub","relation":"software"}]},"title":"Efficient time stepping for numerical integration using reinforcement learning","language":[{"iso":"eng"}],"date_updated":"2023-08-25T09:24:50Z","doi":"10.1137/21M1412682","publication":"SIAM Journal on Scientific Computing","author":[{"first_name":"Michael","full_name":"Dellnitz, Michael","last_name":"Dellnitz"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"first_name":"Marvin","full_name":"Lücke, Marvin","last_name":"Lücke"},{"last_name":"Ober-Blöbaum","id":"16494","first_name":"Sina","full_name":"Ober-Blöbaum, Sina"},{"id":"85279","last_name":"Offen","full_name":"Offen, Christian","orcid":"0000-0002-5940-8057","first_name":"Christian"},{"first_name":"Sebastian","full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","id":"47427"},{"full_name":"Pfannschmidt, Karlson","orcid":"0000-0001-9407-7903","first_name":"Karlson","id":"13472","last_name":"Pfannschmidt"}],"date_created":"2021-04-09T07:59:19Z","status":"public","has_accepted_license":"1","volume":45,"abstract":[{"text":"Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML.","lang":"eng"}],"user_id":"47427","ddc":["510"],"main_file_link":[{"url":"https://epubs.siam.org/doi/reader/10.1137/21M1412682"}],"page":"A579-A595","type":"journal_article","year":"2023","citation":{"mla":"Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration Using Reinforcement Learning.” SIAM Journal on Scientific Computing, vol. 45, no. 2, 2023, pp. A579–95, doi:10.1137/21M1412682.","bibtex":"@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023, title={Efficient time stepping for numerical integration using reinforcement learning}, volume={45}, DOI={10.1137/21M1412682}, number={2}, journal={SIAM Journal on Scientific Computing}, author={Dellnitz, Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, year={2023}, pages={A579–A595} }","apa":"Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. 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Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595."},"_id":"21600","intvolume":" 45","issue":"2"},{"language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-031-45275-8_13","date_updated":"2024-02-26T08:41:49Z","publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783031452741","9783031452758"]},"title":"Probabilistic Scoring Lists for Interpretable Machine Learning","place":"Cham","year":"2023","type":"conference","citation":{"ieee":"J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Probabilistic Scoring Lists for Interpretable Machine Learning,” in 26th International Conference on Discovery Science , Porto, 2023, vol. 14050, pp. 189–203, doi: 10.1007/978-3-031-45275-8_13.","short":"J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, in: 26th International Conference on Discovery Science , Springer Nature Switzerland, Cham, 2023, pp. 189–203.","bibtex":"@inproceedings{Hanselle_Fürnkranz_Hüllermeier_2023, place={Cham}, series={Lecture Notes in Computer Science}, title={Probabilistic Scoring Lists for Interpretable Machine Learning}, volume={14050}, DOI={10.1007/978-3-031-45275-8_13}, booktitle={26th International Conference on Discovery Science }, publisher={Springer Nature Switzerland}, author={Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}, year={2023}, pages={189–203}, collection={Lecture Notes in Computer Science} }","mla":"Hanselle, Jonas Manuel, et al. “Probabilistic Scoring Lists for Interpretable Machine Learning.” 26th International Conference on Discovery Science , vol. 14050, Springer Nature Switzerland, 2023, pp. 189–203, doi:10.1007/978-3-031-45275-8_13.","apa":"Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). 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Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-45275-8_13."},"page":"189-203","_id":"51373","intvolume":" 14050","conference":{"end_date":"2021-10-11","location":"Porto","name":"26th International Conference on Discovery Science ","start_date":"2023-10-9"},"status":"public","date_created":"2024-02-18T11:05:55Z","volume":14050,"publisher":"Springer Nature Switzerland","author":[{"id":"43980","last_name":"Hanselle","full_name":"Hanselle, Jonas Manuel","orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel"},{"first_name":"Johannes","full_name":"Fürnkranz, Johannes","last_name":"Fürnkranz"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication":"26th International Conference on Discovery Science ","user_id":"54779"},{"title":"iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams","user_id":"14931","place":"Cham","publication_identifier":{"isbn":["9783031434174"],"eisbn":["9783031434181"],"issn":["0302-9743"],"eissn":["1611-3349"]},"publication_status":"published","date_created":"2023-11-10T14:11:20Z","project":[{"_id":"126","name":"TRR 318 - C3: TRR 318 - Subproject C3"}],"status":"public","department":[{"_id":"660"}],"publication":"Machine Learning and Knowledge Discovery in Databases: Research Track","publisher":"Springer Nature Switzerland","author":[{"full_name":"Muschalik, Maximilian","first_name":"Maximilian","last_name":"Muschalik"},{"last_name":"Fumagalli","id":"93420","first_name":"Fabian","full_name":"Fumagalli, Fabian"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"},{"id":"48129","last_name":"Huellermeier","full_name":"Huellermeier, Eyke","first_name":"Eyke"}],"doi":"10.1007/978-3-031-43418-1_26","date_updated":"2024-03-05T10:48:40Z","_id":"48776","year":"2023","citation":{"ama":"Muschalik M, Fumagalli F, Hammer B, Huellermeier E. iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. 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Fumagalli, B. Hammer, and E. Huellermeier, “iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams,” in Machine Learning and Knowledge Discovery in Databases: Research Track, Cham: Springer Nature Switzerland, 2023."},"type":"book_chapter","language":[{"iso":"eng"}]},{"publisher":"Springer Nature Switzerland","author":[{"last_name":"Muschalik","full_name":"Muschalik, Maximilian","first_name":"Maximilian"},{"id":"93420","last_name":"Fumagalli","full_name":"Fumagalli, Fabian","first_name":"Fabian"},{"last_name":"Jagtani","full_name":"Jagtani, Rohit","first_name":"Rohit"},{"last_name":"Hammer","first_name":"Barbara","full_name":"Hammer, Barbara"},{"id":"48129","last_name":"Huellermeier","full_name":"Huellermeier, Eyke","first_name":"Eyke"}],"publication":"Communications in Computer and Information Science","department":[{"_id":"660"}],"publication_status":"published","publication_identifier":{"eissn":["9783031440649"],"issn":["1865-0929"],"eisbn":["1865-0937"],"isbn":["9783031440632"]},"status":"public","project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"}],"date_created":"2023-11-10T14:17:17Z","place":"Cham","title":"iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios","user_id":"14931","year":"2023","citation":{"chicago":"Muschalik, Maximilian, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, and Eyke Huellermeier. “IPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios.” In Communications in Computer and Information Science. Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-44064-9_11.","apa":"Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In Communications in Computer and Information Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_11","ama":"Muschalik M, Fumagalli F, Jagtani R, Hammer B, Huellermeier E. iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In: Communications in Computer and Information Science. 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Huellermeier, “iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios,” in Communications in Computer and Information Science, Cham: Springer Nature Switzerland, 2023."},"type":"book_chapter","language":[{"iso":"eng"}],"date_updated":"2024-03-05T10:41:45Z","_id":"48778","doi":"10.1007/978-3-031-44064-9_11"},{"type":"conference","year":"2023","citation":{"apa":"Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 Proceedings. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/esann/2023.es2023-148","ama":"Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. On Feature Removal for Explainability in Dynamic Environments. 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Hammer, in: ESANN 2023 Proceedings, i6doc.com publ., 2023.","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: 10.14428/esann/2023.es2023-148."},"language":[{"iso":"eng"}],"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"},"_id":"48775","date_updated":"2024-03-05T16:47:18Z","publication_identifier":{"unknown":[" 978-2-87587-088-9"]},"publication_status":"published","project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"}],"date_created":"2023-11-10T14:00:08Z","status":"public","department":[{"_id":"660"}],"publication":"ESANN 2023 proceedings","publisher":"i6doc.com publ.","author":[{"id":"93420","last_name":"Fumagalli","full_name":"Fumagalli, Fabian","first_name":"Fabian"},{"full_name":"Muschalik, Maximilian","first_name":"Maximilian","last_name":"Muschalik"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"}],"title":"On Feature Removal for Explainability in Dynamic Environments","user_id":"93420"},{"publisher":"Curran Associates, Inc.","author":[{"first_name":"Fabian","full_name":"Fumagalli, Fabian","last_name":"Fumagalli","id":"93420"},{"full_name":"Muschalik, Maximilian","first_name":"Maximilian","last_name":"Muschalik"},{"last_name":"Kolpaczki","first_name":"Patrick","full_name":"Kolpaczki, Patrick"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"first_name":"Barbara","full_name":"Hammer, Barbara","last_name":"Hammer"}],"publication":"NeurIPS 2023 - Advances in Neural Information Processing Systems","department":[{"_id":"660"}],"volume":36,"status":"public","date_created":"2024-03-01T14:15:31Z","project":[{"name":"TRR 318 - C3: TRR 318 - Subproject C3","_id":"126"},{"grant_number":"438445824","name":"TRR 318: TRR 318 - Erklärbarkeit konstruieren","_id":"109"},{"name":"TRR 318 - C: TRR 318 - Project Area C","_id":"117"}],"title":"SHAP-IQ: Unified Approximation of any-order Shapley Interactions","user_id":"93420","year":"2023","citation":{"ieee":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer, “SHAP-IQ: Unified Approximation of any-order Shapley Interactions,” in NeurIPS 2023 - Advances in Neural Information Processing Systems, 2023, vol. 36, pp. 11515--11551.","short":"F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in: NeurIPS 2023 - Advances in Neural Information Processing Systems, Curran Associates, Inc., 2023, pp. 11515--11551.","bibtex":"@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2023, title={SHAP-IQ: Unified Approximation of any-order Shapley Interactions}, volume={36}, booktitle={NeurIPS 2023 - Advances in Neural Information Processing Systems}, publisher={Curran Associates, Inc.}, 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.” NeurIPS 2023 - Advances in Neural Information Processing Systems, vol. 36, Curran Associates, Inc., 2023, 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 NeurIPS 2023 - Advances in Neural Information Processing Systems, 36:11515--11551. Curran Associates, Inc., 2023.","ama":"Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. SHAP-IQ: Unified Approximation of any-order Shapley Interactions. In: NeurIPS 2023 - Advances in Neural Information Processing Systems. Vol 36. Curran Associates, Inc.; 2023:11515--11551.","apa":"Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. NeurIPS 2023 - Advances in Neural Information Processing Systems, 36, 11515--11551."},"type":"conference","page":"11515--11551","language":[{"iso":"eng"}],"_id":"52230","intvolume":" 36","date_updated":"2024-03-05T16:48:05Z"},{"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"date_created":"2022-04-12T12:00:08Z","status":"public","publication":"arXiv:2202.01651","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"author":[{"last_name":"Schede","full_name":"Schede, Elias","first_name":"Elias"},{"full_name":"Brandt, Jasmin","first_name":"Jasmin","last_name":"Brandt"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","last_name":"Wever","id":"33176"},{"last_name":"Bengs","id":"76599","first_name":"Viktor","full_name":"Bengs, Viktor"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"last_name":"Tierney","first_name":"Kevin","full_name":"Tierney, Kevin"}],"user_id":"38209","title":"A Survey of Methods for Automated Algorithm Configuration","external_id":{"arxiv":["2202.01651"]},"abstract":[{"text":"Algorithm configuration (AC) is concerned with the automated search of the\r\nmost suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently a wide variety of AC problem variants and methods proposed in the\r\nliterature. Existing reviews do not take into account all derivatives of the AC\r\nproblem, nor do they offer a complete classification scheme. To this end, we\r\nintroduce taxonomies to describe the AC problem and features of configuration\r\nmethods, respectively. We review existing AC literature within the lens of our\r\ntaxonomies, outline relevant design choices of configuration approaches,\r\ncontrast methods and problem variants against each other, and describe the\r\nstate of AC in industry. Finally, our review provides researchers and\r\npractitioners with a look at future research directions in the field of AC.","lang":"eng"}],"language":[{"iso":"eng"}],"type":"preprint","citation":{"ieee":"E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.","short":"E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K. Tierney, ArXiv:2202.01651 (2022).","mla":"Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022.","bibtex":"@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651}, author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022} }","chicago":"Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022.","ama":"Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. arXiv:220201651. Published online 2022.","apa":"Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In arXiv:2202.01651."},"year":"2022","date_updated":"2022-04-12T12:01:15Z","_id":"30868"},{"page":"113-123","citation":{"short":"A. Sharma, V. Melnikov, E. Hüllermeier, H. Wehrheim, in: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE, 2022, pp. 113–123.","apa":"Sharma, A., Melnikov, V., Hüllermeier, E., & Wehrheim, H. (2022). Property-Driven Testing of Black-Box Functions. Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 113–123.","ama":"Sharma A, Melnikov V, Hüllermeier E, Wehrheim H. Property-Driven Testing of Black-Box Functions. In: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE). IEEE; 2022:113-123.","chicago":"Sharma, Arnab, Vitaly Melnikov, Eyke Hüllermeier, and Heike Wehrheim. “Property-Driven Testing of Black-Box Functions.” In Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 113–23. IEEE, 2022.","ieee":"A. Sharma, V. Melnikov, E. Hüllermeier, and H. Wehrheim, “Property-Driven Testing of Black-Box Functions,” in Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 2022, pp. 113–123.","mla":"Sharma, Arnab, et al. “Property-Driven Testing of Black-Box Functions.” Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE, 2022, pp. 113–23.","bibtex":"@inproceedings{Sharma_Melnikov_Hüllermeier_Wehrheim_2022, title={Property-Driven Testing of Black-Box Functions}, booktitle={Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)}, publisher={IEEE}, author={Sharma, Arnab and Melnikov, Vitaly and Hüllermeier, Eyke and Wehrheim, Heike}, year={2022}, pages={113–123} }"},"year":"2022","type":"conference","language":[{"iso":"eng"}],"_id":"32311","date_updated":"2022-07-01T11:21:36Z","department":[{"_id":"7"}],"publication":"Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE)","publisher":"IEEE","author":[{"first_name":"Arnab","full_name":"Sharma, Arnab","last_name":"Sharma","id":"67200"},{"first_name":"Vitaly","full_name":"Melnikov, Vitaly","last_name":"Melnikov","id":"58747"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"first_name":"Heike","full_name":"Wehrheim, Heike","last_name":"Wehrheim","id":"573"}],"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B3: SFB 901 - Subproject B3","_id":"11"}],"date_created":"2022-07-01T11:18:03Z","status":"public","abstract":[{"lang":"eng","text":"Testing is one of the most frequent means of quality assurance for software. Property-based testing aims at generating test suites for checking code against user-defined properties. Test input generation is, however, most often independent of the property to be checked, and is instead based on random or user-defined data generation.In this paper, we present property-driven unit testing of functions with numerical inputs and outputs. Alike property-based testing, it allows users to define the properties to be tested for. Contrary to property-based testing, it also uses the property for a targeted generation of test inputs. Our approach is a form of learning-based testing where we first of all learn a model of a given black-box function using standard machine learning algorithms, and in a second step use model and property for test input generation. This allows us to test both predefined functions as well as machine learned regression models. Our experimental evaluation shows that our property-driven approach is more effective than standard property-based testing techniques."}],"title":"Property-Driven Testing of Black-Box Functions","user_id":"477"},{"conference":{"end_date":"2022-11-14","location":"Suzhou, China","name":"International Joint Conference on Rough Sets","start_date":"2022-11-11"},"intvolume":" 13633","_id":"34542","date_updated":"2022-12-19T09:34:44Z","language":[{"iso":"eng"}],"page":"57-70","year":"2022","citation":{"ieee":"A. Campagner, J. Lienen, E. Hüllermeier, and D. Ciucci, “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning,” in Lecture Notes in Computer Science, Suzhou, China, 2022, vol. 13633, pp. 57–70.","short":"A. Campagner, J. Lienen, E. Hüllermeier, D. Ciucci, in: Lecture Notes in Computer Science, Springer, 2022, pp. 57–70.","mla":"Campagner, Andrea, et al. “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.” Lecture Notes in Computer Science, vol. 13633, Springer, 2022, pp. 57–70.","bibtex":"@inproceedings{Campagner_Lienen_Hüllermeier_Ciucci_2022, title={Scikit-Weak: A Python Library for Weakly Supervised Machine Learning}, volume={13633}, booktitle={Lecture Notes in Computer Science}, publisher={Springer}, author={Campagner, Andrea and Lienen, Julian and Hüllermeier, Eyke and Ciucci, Davide}, year={2022}, pages={57–70} }","ama":"Campagner A, Lienen J, Hüllermeier E, Ciucci D. Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. In: Lecture Notes in Computer Science. Vol 13633. Springer; 2022:57-70.","apa":"Campagner, A., Lienen, J., Hüllermeier, E., & Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. Lecture Notes in Computer Science, 13633, 57–70.","chicago":"Campagner, Andrea, Julian Lienen, Eyke Hüllermeier, and Davide Ciucci. “Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.” In Lecture Notes in Computer Science, 13633:57–70. Springer, 2022."},"type":"conference","user_id":"44040","title":"Scikit-Weak: A Python Library for Weakly Supervised Machine Learning","publication":"Lecture Notes in Computer Science","author":[{"first_name":"Andrea","full_name":"Campagner, Andrea","last_name":"Campagner"},{"first_name":"Julian","full_name":"Lienen, Julian","last_name":"Lienen","id":"44040"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"full_name":"Ciucci, Davide","first_name":"Davide","last_name":"Ciucci"}],"publisher":"Springer","date_created":"2022-12-19T09:34:35Z","status":"public","volume":13633},{"oa":"1","_id":"31546","date_updated":"2022-05-31T07:05:54Z","language":[{"iso":"eng"}],"type":"preprint","year":"2022","citation":{"short":"J. Lienen, C. Demir, E. Hüllermeier, ArXiv:2205.15239 (2022).","ieee":"J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised Learning,” arXiv:2205.15239. 2022.","chicago":"Lienen, Julian, Caglar Demir, and Eyke Hüllermeier. “Conformal Credal Self-Supervised Learning.” ArXiv:2205.15239, 2022.","ama":"Lienen J, Demir C, Hüllermeier E. Conformal Credal Self-Supervised Learning. arXiv:220515239. Published online 2022.","apa":"Lienen, J., Demir, C., & Hüllermeier, E. (2022). Conformal Credal Self-Supervised Learning. In arXiv:2205.15239.","mla":"Lienen, Julian, et al. “Conformal Credal Self-Supervised Learning.” ArXiv:2205.15239, 2022.","bibtex":"@article{Lienen_Demir_Hüllermeier_2022, title={Conformal Credal Self-Supervised Learning}, journal={arXiv:2205.15239}, author={Lienen, Julian and Demir, Caglar and Hüllermeier, Eyke}, year={2022} }"},"main_file_link":[{"url":"https://arxiv.org/abs/2205.15239","open_access":"1"}],"user_id":"44040","title":"Conformal Credal Self-Supervised Learning","abstract":[{"lang":"eng","text":"In semi-supervised learning, the paradigm of self-training refers to the idea of learning from pseudo-labels suggested by the learner itself. Across various domains, corresponding methods have proven effective and achieve state-of-the-art performance. However, pseudo-labels typically stem from ad-hoc heuristics, relying on the quality of the predictions though without guaranteeing their validity. One such method, so-called credal self-supervised learning, maintains pseudo-supervision in the form of sets of (instead of single) probability distributions over labels, thereby allowing for a flexible yet uncertainty-aware labeling. Again, however, there is no justification beyond empirical effectiveness. To address this deficiency, we make use of conformal prediction, an approach that comes with guarantees on the validity of set-valued predictions. As a result, the construction of credal sets of labels is supported by a rigorous theoretical foundation, leading to better calibrated and less error-prone supervision for unlabeled data. Along with this, we present effective algorithms for learning from credal self-supervision. An empirical study demonstrates excellent calibration properties of the pseudo-supervision, as well as the competitiveness of our method on several benchmark datasets."}],"status":"public","date_created":"2022-05-31T07:05:36Z","author":[{"last_name":"Lienen","id":"44040","first_name":"Julian","full_name":"Lienen, Julian"},{"id":"43817","last_name":"Demir","full_name":"Demir, Caglar","first_name":"Caglar"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publication":"arXiv:2205.15239"},{"year":"2022","citation":{"mla":"Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI, 2022.","bibtex":"@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }","chicago":"Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.","ama":"Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial Intelligence. Published online 2022.","apa":"Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI.","ieee":"A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.","short":"A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022)."},"type":"preprint","language":[{"iso":"eng"}],"_id":"30867","date_updated":"2022-08-24T12:44:27Z","status":"public","date_created":"2022-04-12T11:58:56Z","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"author":[{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Viktor","full_name":"Bengs, Viktor","last_name":"Bengs","id":"76599"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publisher":"AAAI","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","title":"Machine Learning for Online Algorithm Selection under Censored Feedback","user_id":"38209","abstract":[{"lang":"eng","text":"In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass are presented to an agent one after another, and the agent has to quickly\r\nselect a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence, machine learning methods used to optimize\r\nan algorithm selection strategy in a data-driven manner need to deal with\r\nright-censored samples, a problem that has received little attention in the\r\nliterature so far. In this work, we revisit multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses, allowing for partially censored\r\ndata while keeping a space- and time-complexity independent of the time\r\nhorizon. In an extensive experimental evaluation on an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically well-founded methods based\r\non Thompson sampling perform specifically strong and improve in comparison to\r\nexisting methods."}],"external_id":{"arxiv":["2109.06234"]}},{"external_id":{"arxiv":["2107.09414"]},"abstract":[{"lang":"eng","text":"The problem of selecting an algorithm that appears most suitable for a\r\nspecific instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability problem, is called instance-specific algorithm selection. Over\r\nthe past decade, the problem has received considerable attention, resulting in\r\na number of different methods for algorithm selection. Although most of these\r\nmethods are based on machine learning, surprisingly little work has been done\r\non meta learning, that is, on taking advantage of the complementarity of\r\nexisting algorithm selection methods in order to combine them into a single\r\nsuperior algorithm selector. In this paper, we introduce the problem of meta\r\nalgorithm selection, which essentially asks for the best way to combine a given\r\nset of algorithm selectors. We present a general methodological framework for\r\nmeta algorithm selection as well as several concrete learning methods as\r\ninstantiations of this framework, essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors can significantly outperform single\r\nalgorithm selectors and have the potential to form the new state of the art in\r\nalgorithm selection."}],"user_id":"38209","title":"Algorithm Selection on a Meta Level","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"first_name":"Lukas","full_name":"Gehring, Lukas","last_name":"Gehring"},{"first_name":"Tanja","full_name":"Tornede, Tanja","last_name":"Tornede","id":"40795"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"publication":"Machine Learning","status":"public","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"date_created":"2022-04-12T11:55:18Z","date_updated":"2022-08-24T12:45:39Z","_id":"30865","language":[{"iso":"eng"}],"year":"2022","type":"preprint","citation":{"bibtex":"@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2022} }","mla":"Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.","apa":"Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In Machine Learning.","ama":"Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. Machine Learning. Published online 2022.","chicago":"Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.","ieee":"A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022.","short":"A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022)."}},{"type":"journal_article","citation":{"ieee":"K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,” Welding in the World, 2022, doi: 10.1007/s40194-022-01339-9.","short":"K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022).","mla":"Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.","bibtex":"@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}, DOI={10.1007/s40194-022-01339-9}, journal={Welding in the World}, publisher={Springer Science and Business Media LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }","chicago":"Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner, and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, 2022. https://doi.org/10.1007/s40194-022-01339-9.","apa":"Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. https://doi.org/10.1007/s40194-022-01339-9","ama":"Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. Published online 2022. doi:10.1007/s40194-022-01339-9"},"year":"2022","language":[{"iso":"eng"}],"doi":"10.1007/s40194-022-01339-9","date_updated":"2022-08-24T12:52:06Z","_id":"33090","publication_identifier":{"issn":["0043-2288","1878-6669"]},"publication_status":"published","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"date_created":"2022-08-24T12:51:07Z","status":"public","publication":"Welding in the World","keyword":["Metals and Alloys","Mechanical Engineering","Mechanics of Materials"],"publisher":"Springer Science and Business Media LLC","author":[{"id":"83151","last_name":"Gevers","full_name":"Gevers, Karina","first_name":"Karina"},{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"last_name":"Schöppner","id":"20530","first_name":"Volker","full_name":"Schöppner, Volker"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"title":"A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials","user_id":"38209","abstract":[{"lang":"eng","text":"AbstractHeated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality."}]},{"user_id":"40795","title":"Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens","ddc":["004"],"status":"public","has_accepted_license":"1","date_created":"2023-01-11T15:00:00Z","author":[{"last_name":"Hammer","first_name":"Barbara","full_name":"Hammer, Barbara"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"last_name":"Lohweg","first_name":"Volker","full_name":"Lohweg, Volker"},{"full_name":"Schneider, Alexander","first_name":"Alexander","last_name":"Schneider"},{"first_name":"Wolfram","full_name":"Schenck, Wolfram","last_name":"Schenck"},{"first_name":"Ulrike","full_name":"Kuhl, Ulrike","last_name":"Kuhl"},{"first_name":"Marco","full_name":"Braun, Marco","last_name":"Braun"},{"last_name":"Pfeifer","full_name":"Pfeifer, Anton","first_name":"Anton"},{"last_name":"Holst","full_name":"Holst, Christoph-Alexander","first_name":"Christoph-Alexander"},{"first_name":"Malte","full_name":"Schmidt, Malte","last_name":"Schmidt"},{"full_name":"Schomaker, Gunnar","first_name":"Gunnar","last_name":"Schomaker"},{"full_name":"Tornede, Tanja","first_name":"Tanja","id":"40795","last_name":"Tornede"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"534"}],"doi":"10.4119/unibi/2965622","date_updated":"2023-01-11T15:20:40Z","_id":"36227","language":[{"iso":"ger"}],"year":"2022","type":"report","citation":{"short":"B. Hammer, E. Hüllermeier, V. Lohweg, A. Schneider, W. Schenck, U. Kuhl, M. Braun, A. Pfeifer, C.-A. Holst, M. Schmidt, G. Schomaker, T. Tornede, Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens, 2022.","ama":"Hammer B, Hüllermeier E, Lohweg V, et al. Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens.; 2022. doi:10.4119/unibi/2965622","apa":"Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl, U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., & Tornede, T. (2022). Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. https://doi.org/10.4119/unibi/2965622","chicago":"Hammer, Barbara, Eyke Hüllermeier, Volker Lohweg, Alexander Schneider, Wolfram Schenck, Ulrike Kuhl, Marco Braun, et al. Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens, 2022. https://doi.org/10.4119/unibi/2965622.","ieee":"B. Hammer et al., Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022.","mla":"Hammer, Barbara, et al. Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022, doi:10.4119/unibi/2965622.","bibtex":"@book{Hammer_Hüllermeier_Lohweg_Schneider_Schenck_Kuhl_Braun_Pfeifer_Holst_Schmidt_et al._2022, title={Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens}, DOI={10.4119/unibi/2965622}, author={Hammer, Barbara and Hüllermeier, Eyke and Lohweg, Volker and Schneider, Alexander and Schenck, Wolfram and Kuhl, Ulrike and Braun, Marco and Pfeifer, Anton and Holst, Christoph-Alexander and Schmidt, Malte and et al.}, year={2022} }"}}]