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Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023. https://doi.org/10.17619/UNIPB/1-1780 .","apa":"Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. https://doi.org/10.17619/UNIPB/1-1780 ","ama":"Tornede A. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.; 2023. doi:10.17619/UNIPB/1-1780 ","short":"A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.","bibtex":"@book{Tornede_2023, title={Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions}, DOI={10.17619/UNIPB/1-1780 }, author={Tornede, Alexander}, year={2023} }","mla":"Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023, doi:10.17619/UNIPB/1-1780 .","ieee":"A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023."},"supervisor":[{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}]},{"intvolume":" 45","_id":"21600","issue":"2","main_file_link":[{"url":"https://epubs.siam.org/doi/reader/10.1137/21M1412682"}],"page":"A579-A595","type":"journal_article","citation":{"ieee":"M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement learning,” SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.","short":"M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595.","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} }","chicago":"Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping for Numerical Integration Using Reinforcement Learning.” SIAM Journal on Scientific Computing 45, no. 2 (2023): A579–95. https://doi.org/10.1137/21M1412682.","ama":"Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement learning. SIAM Journal on Scientific Computing. 2023;45(2):A579-A595. doi:10.1137/21M1412682","apa":"Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682"},"year":"2023","abstract":[{"lang":"eng","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."}],"user_id":"47427","ddc":["510"],"publication":"SIAM Journal on Scientific Computing","author":[{"first_name":"Michael","full_name":"Dellnitz, Michael","last_name":"Dellnitz"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"last_name":"Lücke","full_name":"Lücke, Marvin","first_name":"Marvin"},{"full_name":"Ober-Blöbaum, Sina","first_name":"Sina","id":"16494","last_name":"Ober-Blöbaum"},{"first_name":"Christian","orcid":"0000-0002-5940-8057","full_name":"Offen, Christian","last_name":"Offen","id":"85279"},{"id":"47427","last_name":"Peitz","full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","first_name":"Sebastian"},{"id":"13472","last_name":"Pfannschmidt","orcid":"0000-0001-9407-7903","full_name":"Pfannschmidt, Karlson","first_name":"Karlson"}],"date_created":"2021-04-09T07:59:19Z","status":"public","has_accepted_license":"1","volume":45,"date_updated":"2023-08-25T09:24:50Z","doi":"10.1137/21M1412682","language":[{"iso":"eng"}],"external_id":{"arxiv":["arXiv:2104.03562"]},"related_material":{"link":[{"url":"https://github.com/lueckem/quadrature-ML","relation":"software","description":"GitHub"}]},"title":"Efficient time stepping for numerical integration using reinforcement learning","department":[{"_id":"101"},{"_id":"636"},{"_id":"355"},{"_id":"655"}],"publication_status":"published"},{"conference":{"location":"online","name":"International Institute of Welding","start_date":"2021-07-12","end_date":"2021-07-14"},"_id":"24382","date_updated":"2022-01-06T06:56:19Z","type":"conference","citation":{"short":"K. Gevers, V. Schöppner, E. Hüllermeier, in: 2021.","ieee":"K. Gevers, V. Schöppner, and E. Hüllermeier, “Heated tool butt welding of two different materials – Established methods versus artificial intelligence,” presented at the International Institute of Welding, online, 2021.","chicago":"Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials – Established Methods versus Artificial Intelligence,” 2021.","ama":"Gevers K, Schöppner V, Hüllermeier E. Heated tool butt welding of two different materials – Established methods versus artificial intelligence. In: ; 2021.","apa":"Gevers, K., Schöppner, V., & Hüllermeier, E. (2021). Heated tool butt welding of two different materials – Established methods versus artificial intelligence. International Institute of Welding, online.","bibtex":"@inproceedings{Gevers_Schöppner_Hüllermeier_2021, title={Heated tool butt welding of two different materials – Established methods versus artificial intelligence}, author={Gevers, Karina and Schöppner, Volker and Hüllermeier, Eyke}, year={2021} }","mla":"Gevers, Karina, et al. Heated Tool Butt Welding of Two Different Materials – Established Methods versus Artificial Intelligence. 2021."},"year":"2021","language":[{"iso":"eng"}],"title":"Heated tool butt welding of two different materials – Established methods versus artificial intelligence","user_id":"83151","department":[{"_id":"367"},{"_id":"355"},{"_id":"321"}],"author":[{"full_name":"Gevers, Karina","first_name":"Karina","id":"83151","last_name":"Gevers"},{"last_name":"Schöppner","id":"20530","first_name":"Volker","full_name":"Schöppner, Volker"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"date_created":"2021-09-14T11:34:31Z","status":"public"},{"date_updated":"2022-01-06T06:54:42Z","_id":"21004","doi":"10.1109/tpami.2021.3051276","page":"1-1","citation":{"apa":"Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276."},"type":"journal_article","year":"2021","language":[{"iso":"eng"}],"abstract":[{"text":"Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.","lang":"eng"}],"title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","user_id":"5786","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"publication_status":"published","date_created":"2021-01-16T14:48:13Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"status":"public"},{"publication_status":"accepted","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2021-01-27T13:45:52Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","publisher":"IEEE","author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"title":"Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning","user_id":"5786","abstract":[{"text":"Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout.\r\nIn this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions.","lang":"eng"}],"citation":{"short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.).","ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence.","apa":"Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.","ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d.","mla":"Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.","bibtex":"@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke} }"},"year":"2021","type":"journal_article","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:54:45Z","_id":"21092"},{"author":[{"first_name":"Viktor","full_name":"Bengs, Viktor","last_name":"Bengs"},{"last_name":"Busa-Fekete","full_name":"Busa-Fekete, Róbert","first_name":"Róbert"},{"last_name":"El Mesaoudi-Paul","first_name":"Adil","full_name":"El Mesaoudi-Paul, Adil"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"quality_controlled":"1","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"Journal of Machine Learning Research","volume":22,"status":"public","date_created":"2021-03-18T11:15:38Z","title":"Preference-based Online Learning with Dueling Bandits: A Survey","user_id":"76599","type":"journal_article","citation":{"ieee":"V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, and E. Hüllermeier, “Preference-based Online Learning with Dueling Bandits: A Survey,” Journal of Machine Learning Research, vol. 22, no. 7, pp. 1–108, 2021.","short":"V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, E. Hüllermeier, Journal of Machine Learning Research 22 (2021) 1–108.","mla":"Bengs, Viktor, et al. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research, vol. 22, no. 7, 2021, pp. 1–108.","bibtex":"@article{Bengs_Busa-Fekete_El Mesaoudi-Paul_Hüllermeier_2021, title={Preference-based Online Learning with Dueling Bandits: A Survey}, volume={22}, number={7}, journal={Journal of Machine Learning Research}, author={Bengs, Viktor and Busa-Fekete, Róbert and El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}, year={2021}, pages={1–108} }","apa":"Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., & Hüllermeier, E. (2021). Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research, 22(7), 1–108.","ama":"Bengs V, Busa-Fekete R, El Mesaoudi-Paul A, Hüllermeier E. Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research. 2021;22(7):1-108.","chicago":"Bengs, Viktor, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research 22, no. 7 (2021): 1–108."},"year":"2021","page":"1-108","language":[{"iso":"eng"}],"intvolume":" 22","_id":"21535","date_updated":"2022-01-06T06:55:03Z","issue":"7"},{"conference":{"name":"Genetic and Evolutionary Computation Conference","start_date":"2021-07-10","end_date":"2021-07-14"},"date_updated":"2022-01-06T06:55:06Z","_id":"21570","citation":{"ieee":"T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021.","short":"T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","bibtex":"@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021} }","mla":"Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","ama":"Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings of the Genetic and Evolutionary Computation Conference. ; 2021.","apa":"Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference, 2021."},"type":"conference","year":"2021","language":[{"iso":"eng"}],"title":"Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance","user_id":"5786","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"id":"40795","last_name":"Tornede","full_name":"Tornede, Tanja","first_name":"Tanja"},{"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"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2021-03-26T09:14:19Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"status":"public"},{"type":"conference","citation":{"short":"R. Bernijazov, A. Dicks, R. Dumitrescu, M. Foullois, J.M. Hanselle, E. Hüllermeier, G. Karakaya, P. Ködding, V. Lohweg, M. Malatyali, F. Meyer auf der Heide, M. Panzner, C. Soltenborn, in: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.","ieee":"R. Bernijazov et al., “A Meta-Review on Artificial Intelligence in Product Creation,” presented at the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada, 2021.","ama":"Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence in Product Creation. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). ; 2021.","apa":"Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.","chicago":"Bernijazov, Ruslan, Alexander Dicks, Roman Dumitrescu, Marc Foullois, Jonas Manuel Hanselle, Eyke Hüllermeier, Gökce Karakaya, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.","mla":"Bernijazov, Ruslan, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.","bibtex":"@inproceedings{Bernijazov_Dicks_Dumitrescu_Foullois_Hanselle_Hüllermeier_Karakaya_Ködding_Lohweg_Malatyali_et al._2021, title={A Meta-Review on Artificial Intelligence in Product Creation}, booktitle={Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)}, author={Bernijazov, Ruslan and Dicks, Alexander and Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali, Manuel and et al.}, year={2021} }"},"year":"2021","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://www.hsu-hh.de/imb/wp-content/uploads/sites/677/2021/08/A-Meta-Review-on-Artificial-Intelligence-in-Product-Creation.pdf"}],"date_updated":"2022-01-06T06:55:59Z","_id":"23779","conference":{"location":"Montreal, Kanada","name":"30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop \"AI and Product Design\"","start_date":"2021-08-19","end_date":"2021-08-26"},"publication_status":"epub_ahead","status":"public","date_created":"2021-09-06T08:23:45Z","author":[{"first_name":"Ruslan","full_name":"Bernijazov, Ruslan","last_name":"Bernijazov"},{"last_name":"Dicks","first_name":"Alexander","full_name":"Dicks, Alexander"},{"id":"16190","last_name":"Dumitrescu","full_name":"Dumitrescu, Roman","first_name":"Roman"},{"last_name":"Foullois","full_name":"Foullois, Marc","first_name":"Marc"},{"first_name":"Jonas Manuel","orcid":"0000-0002-1231-4985","full_name":"Hanselle, Jonas Manuel","last_name":"Hanselle","id":"43980"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"first_name":"Gökce","full_name":"Karakaya, Gökce","last_name":"Karakaya"},{"last_name":"Ködding","id":"45402","first_name":"Patrick","full_name":"Ködding, Patrick"},{"last_name":"Lohweg","first_name":"Volker","full_name":"Lohweg, Volker"},{"first_name":"Manuel","full_name":"Malatyali, Manuel","last_name":"Malatyali","id":"41265"},{"first_name":"Friedhelm","full_name":"Meyer auf der Heide, Friedhelm","last_name":"Meyer auf der Heide","id":"15523"},{"last_name":"Panzner","full_name":"Panzner, Melina","first_name":"Melina"},{"last_name":"Soltenborn","id":"1737","first_name":"Christian","orcid":"0000-0002-0342-8227","full_name":"Soltenborn, Christian"}],"quality_controlled":"1","publication":"Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)","keyword":["Artificial Intelligence Product Creation Literature Review"],"department":[{"_id":"63"},{"_id":"563"},{"_id":"355"},{"_id":"241"}],"title":"A Meta-Review on Artificial Intelligence in Product Creation","user_id":"15415","abstract":[{"text":"Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. \r\nIm Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden.","lang":"ger"},{"text":"Product Creation (PC) refers to the process of planning and developing a product as well as related services from the initial idea until manufacturing and distribution. Throughout this process, there are numerous tasks that depend on human expertise and are typically undertaken by experienced practitioners. As the field of Artificial Intelligence (AI) continues to evolve and finds its way into the manufacturing sector, there exist many possibilities for an application of AI in order to assist in solving aforementioned tasks. In this work, we provide a comprehensive overview of the current state of the art of the use of AI in PC. \r\nIn detail, we analyze 40 existing surveys on AI in PC and 94 case studies in order to find out which areas of PC are primarily addressed by current research in this field, how mature the discussed AI methods are, and to which extent data-centric approaches are utilized in current research.","lang":"eng"}]},{"user_id":"5786","title":"Automated Machine Learning, Bounded Rationality, and Rational Metareasoning","quality_controlled":"1","author":[{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","date_created":"2021-08-02T07:46:29Z","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"_id":"22913","date_updated":"2022-01-06T06:55:43Z","conference":{"end_date":"2021-09-17","start_date":"2021-09-13","name":"ECML/PKDD Workshop on Automating Data Science","location":"Bilbao (Virtual)"},"language":[{"iso":"eng"}],"citation":{"ieee":"E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021.","short":"E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.","bibtex":"@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021} }","mla":"Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021.","chicago":"Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021.","apa":"Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).","ama":"Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021."},"year":"2021","type":"conference"},{"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"}],"date_created":"2021-08-02T07:48:07Z","status":"public","user_id":"5786","title":"Replacing the Ex-Def Baseline in AutoML by Naive AutoML","language":[{"iso":"eng"}],"year":"2021","type":"conference","citation":{"short":"F. Mohr, M.D. Wever, in: 2021.","ieee":"F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual, 2021.","chicago":"Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021.","apa":"Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.","ama":"Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. In: ; 2021.","mla":"Mohr, Felix, and Marcel Dominik Wever. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 2021.","bibtex":"@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021} }"},"conference":{"end_date":"2021-07-23","location":"Virtual","start_date":"2021-07-23","name":"8th ICML Workshop on Automated Machine Learning"},"date_updated":"2022-01-06T06:55:43Z","_id":"22914"},{"user_id":"48192","abstract":[{"lang":"eng","text":"Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression."}],"volume":12986,"date_created":"2021-11-11T14:15:18Z","status":"public","publication":"Proceedings of The 24th International Conference on Discovery Science (DS 2021)","keyword":["Graph-structured data","Graph neural networks","Preference learning","Learning to rank"],"author":[{"first_name":"Clemens","orcid":"0000-0002-0455-0048","full_name":"Damke, Clemens","last_name":"Damke","id":"48192"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"publisher":"Springer","quality_controlled":"1","conference":{"name":"24th International Conference on Discovery Science","start_date":"2021-10-11","location":"Halifax, Canada","end_date":"2021-10-13"},"intvolume":" 12986","_id":"27381","page":"166-180","citation":{"chicago":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” In Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021. https://doi.org/10.1007/978-3-030-88942-5.","apa":"Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer. https://doi.org/10.1007/978-3-030-88942-5","ama":"Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5","mla":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer, 2021, pp. 166–80, doi:10.1007/978-3-030-88942-5.","bibtex":"@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986}, DOI={10.1007/978-3-030-88942-5}, booktitle={Proceedings of The 24th International Conference on Discovery Science (DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke}, editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture Notes in Computer Science} }","short":"C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021), Springer, 2021, pp. 166–180.","ieee":"C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural Networks,” in Proceedings of The 24th International Conference on Discovery Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5."},"type":"conference","year":"2021","title":"Ranking Structured Objects with Graph Neural Networks","external_id":{"arxiv":["2104.08869"]},"publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030889418","9783030889425"]},"publication_status":"published","editor":[{"full_name":"Soares, Carlos","first_name":"Carlos","last_name":"Soares"},{"full_name":"Torgo, Luis","first_name":"Luis","last_name":"Torgo"}],"department":[{"_id":"355"}],"doi":"10.1007/978-3-030-88942-5","date_updated":"2022-04-11T22:08:12Z","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science"},{"_id":"27284","supervisor":[{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"citation":{"bibtex":"@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification}, DOI={10.17619/UNIPB/1-1302}, author={Wever, Marcel Dominik}, year={2021} }","mla":"Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification. 2021, doi:10.17619/UNIPB/1-1302.","chicago":"Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification, 2021. https://doi.org/10.17619/UNIPB/1-1302.","ama":"Wever MD. Automated Machine Learning for Multi-Label Classification.; 2021. doi:10.17619/UNIPB/1-1302","apa":"Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification. https://doi.org/10.17619/UNIPB/1-1302","ieee":"M. D. Wever, Automated Machine Learning for Multi-Label Classification. 2021.","short":"M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021."},"type":"dissertation","year":"2021","user_id":"33176","ddc":["000"],"date_created":"2021-11-08T14:05:19Z","status":"public","has_accepted_license":"1","file":[{"file_size":8098177,"file_id":"30886","creator":"wever","content_type":"application/pdf","date_updated":"2022-04-13T09:39:56Z","relation":"main_file","file_name":"dissertation_publish_upload.pdf","date_created":"2022-04-13T09:35:25Z","access_level":"open_access"}],"file_date_updated":"2022-04-13T09:39:56Z","author":[{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"}],"oa":"1","doi":"10.17619/UNIPB/1-1302","date_updated":"2022-04-13T09:39:56Z","language":[{"iso":"eng"}],"title":"Automated Machine Learning for Multi-Label Classification","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"publication_status":"published","department":[{"_id":"355"}]},{"title":"Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data","user_id":"38209","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2021-02-09T09:30:14Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"last_name":"Hanselle","id":"43980","first_name":"Jonas Manuel","full_name":"Hanselle, Jonas Manuel","orcid":"0000-0002-1231-4985"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"conference":{"start_date":"2021-05-11","name":"The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)","location":"Delhi, India","end_date":"2021-05-14"},"_id":"21198","date_updated":"2022-08-24T12:49:06Z","citation":{"chicago":"Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” PAKDD, 2021.","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.","apa":"Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.","bibtex":"@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }","mla":"Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. 2021.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).","ieee":"J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” 2021."},"year":"2021","type":"conference","language":[{"iso":"eng"}],"series_title":"PAKDD"},{"place":"Cham","title":"Learning Choice Functions via Pareto-Embeddings","user_id":"13472","department":[{"_id":"7"},{"_id":"355"}],"publication":"Lecture Notes in Computer Science","author":[{"last_name":"Pfannschmidt","full_name":"Pfannschmidt, Karlson","first_name":"Karlson"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030582845","9783030582852"]},"project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2020-09-17T10:52:41Z","status":"public","date_updated":"2022-01-06T06:54:06Z","_id":"19521","doi":"10.1007/978-3-030-58285-2_30","year":"2020","citation":{"bibtex":"@inbook{Pfannschmidt_Hüllermeier_2020, place={Cham}, title={Learning Choice Functions via Pareto-Embeddings}, DOI={10.1007/978-3-030-58285-2_30}, booktitle={Lecture Notes in Computer Science}, author={Pfannschmidt, Karlson and Hüllermeier, Eyke}, year={2020} }","mla":"Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” Lecture Notes in Computer Science, 2020, doi:10.1007/978-3-030-58285-2_30.","ama":"Pfannschmidt K, Hüllermeier E. Learning Choice Functions via Pareto-Embeddings. In: Lecture Notes in Computer Science. Cham; 2020. doi:10.1007/978-3-030-58285-2_30","apa":"Pfannschmidt, K., & Hüllermeier, E. (2020). Learning Choice Functions via Pareto-Embeddings. In Lecture Notes in Computer Science. Cham. https://doi.org/10.1007/978-3-030-58285-2_30","chicago":"Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” In Lecture Notes in Computer Science. Cham, 2020. https://doi.org/10.1007/978-3-030-58285-2_30.","ieee":"K. Pfannschmidt and E. Hüllermeier, “Learning Choice Functions via Pareto-Embeddings,” in Lecture Notes in Computer Science, Cham, 2020.","short":"K. Pfannschmidt, E. Hüllermeier, in: Lecture Notes in Computer Science, Cham, 2020."},"type":"book_chapter","language":[{"iso":"eng"}]},{"external_id":{"arxiv":["2007.00346"]},"place":"Bangkok, Thailand","title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution","department":[{"_id":"355"}],"publication_status":"published","editor":[{"first_name":"Sinno","full_name":"Jialin Pan, Sinno","last_name":"Jialin Pan"},{"last_name":"Sugiyama","full_name":"Sugiyama, Masashi","first_name":"Masashi"}],"date_updated":"2022-01-06T06:54:17Z","oa":"1","series_title":"Proceedings of Machine Learning Research","language":[{"iso":"eng"}],"abstract":[{"text":"Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.","lang":"eng"}],"ddc":["006"],"user_id":"48192","keyword":["graph neural networks","Weisfeiler-Lehman test","cycle detection"],"file_date_updated":"2020-10-08T11:24:29Z","publication":"Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)","quality_controlled":"1","author":[{"last_name":"Damke","id":"48192","first_name":"Clemens","full_name":"Damke, Clemens","orcid":"0000-0002-0455-0048"},{"full_name":"Melnikov, Vitaly","first_name":"Vitaly","id":"58747","last_name":"Melnikov"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publisher":"PMLR","file":[{"date_created":"2020-10-08T10:54:48Z","file_name":"damke20.pdf","access_level":"open_access","file_size":771137,"file_id":"19954","creator":"cdamke","date_updated":"2020-10-08T11:21:00Z","content_type":"application/pdf","relation":"main_file"},{"access_level":"open_access","file_name":"damke20-supp.pdf","date_created":"2020-10-08T10:54:59Z","date_updated":"2020-10-08T11:24:29Z","content_type":"application/pdf","relation":"supplementary_material","file_size":613163,"file_id":"19955","creator":"cdamke"}],"volume":129,"date_created":"2020-10-08T10:48:38Z","has_accepted_license":"1","status":"public","conference":{"location":"Bangkok, Thailand","name":"Asian Conference on Machine Learning","start_date":"2020-11-18","end_date":"2020-11-20"},"intvolume":" 129","_id":"19953","page":"49-64","type":"conference","citation":{"mla":"Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.","bibtex":"@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand}, series={Proceedings of Machine Learning Research}, title={A Novel Higher-order Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR}, author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings of Machine Learning Research} }","chicago":"Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020.","ama":"Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.","apa":"Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.","ieee":"C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman Graph Convolution,” in Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64.","short":"C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR, Bangkok, Thailand, 2020, pp. 49–64."},"year":"2020"},{"_id":"21534","date_updated":"2022-01-06T06:55:03Z","citation":{"short":"V. Bengs, E. Hüllermeier, in: International Conference on Machine Learning, 2020, pp. 778–787.","ieee":"V. Bengs and E. Hüllermeier, “Preselection Bandits,” in International Conference on Machine Learning, 2020, pp. 778–787.","chicago":"Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International Conference on Machine Learning, 778–87, 2020.","apa":"Bengs, V., & Hüllermeier, E. (2020). Preselection Bandits. In International Conference on Machine Learning (pp. 778–787).","ama":"Bengs V, Hüllermeier E. Preselection Bandits. 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Bengs and E. Hüllermeier, “Multi-Armed Bandits with Censored Consumption of Resources,” arXiv:2011.00813. 2020.","short":"V. Bengs, E. Hüllermeier, ArXiv:2011.00813 (2020).","bibtex":"@article{Bengs_Hüllermeier_2020, title={Multi-Armed Bandits with Censored Consumption of Resources}, journal={arXiv:2011.00813}, author={Bengs, Viktor and Hüllermeier, Eyke}, year={2020} }","mla":"Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.","chicago":"Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.","ama":"Bengs V, Hüllermeier E. Multi-Armed Bandits with Censored Consumption of Resources. arXiv:201100813. 2020.","apa":"Bengs, V., & Hüllermeier, E. (2020). Multi-Armed Bandits with Censored Consumption of Resources. ArXiv:2011.00813."},"abstract":[{"text":"We consider a resource-aware variant of the classical multi-armed bandit\r\nproblem: In each round, the learner selects an arm and determines a resource\r\nlimit. It then observes a corresponding (random) reward, provided the (random)\r\namount of consumed resources remains below the limit. Otherwise, the\r\nobservation is censored, i.e., no reward is obtained. For this problem setting,\r\nwe introduce a measure of regret, which incorporates the actual amount of\r\nallocated resources of each learning round as well as the optimality of\r\nrealizable rewards. Thus, to minimize regret, the learner needs to set a\r\nresource limit and choose an arm in such a way that the chance to realize a\r\nhigh reward within the predefined resource limit is high, while the resource\r\nlimit itself should be kept as low as possible. We derive the theoretical lower\r\nbound on the cumulative regret and propose a learning algorithm having a regret\r\nupper bound that matches the lower bound. In a simulation study, we show that\r\nour learning algorithm outperforms straightforward extensions of standard\r\nmulti-armed bandit algorithms.","lang":"eng"}],"user_id":"76599","title":"Multi-Armed Bandits with Censored Consumption of Resources","publication":"arXiv:2011.00813","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"author":[{"first_name":"Viktor","full_name":"Bengs, Viktor","last_name":"Bengs"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2021-03-18T11:27:37Z","status":"public"},{"language":[{"iso":"eng"}],"year":"2020","type":"conference","citation":{"apa":"Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.","ama":"Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In Discovery Science, 2020.","mla":"Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020.","bibtex":"@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Extreme Algorithm Selection with Dyadic Feature Representation}, booktitle={Discovery Science}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection with Dyadic Feature Representation,” presented at the Discovery Science 2020, 2020."},"_id":"17407","date_updated":"2022-01-06T06:53:10Z","conference":{"name":"Discovery Science 2020"},"status":"public","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2020-07-21T10:06:51Z","author":[{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"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"}],"publication":"Discovery Science","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","title":"Extreme Algorithm Selection with Dyadic Feature Representation"},{"type":"conference","year":"2020","citation":{"bibtex":"@inproceedings{Hanselle_Tornede_Wever_Hüllermeier_2020, title={Hybrid Ranking and Regression for Algorithm Selection}, booktitle={KI 2020: Advances in Artificial Intelligence}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","mla":"Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.","chicago":"Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI 2020: Advances in Artificial Intelligence, 2020.","ama":"Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence. ; 2020.","apa":"Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.","ieee":"J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking and Regression for Algorithm Selection,” presented at the 43rd German Conference on Artificial Intelligence, 2020.","short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. 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