--- _id: '45884' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo - first_name: Mohamed full_name: Sherif, Mohamed id: '67234' last_name: Sherif orcid: https://orcid.org/0000-0002-9927-2203 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: 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 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} }' 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.' 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.' 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. 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.' date_created: 2023-07-07T07:50:53Z date_updated: 2023-07-07T11:20:12Z ddc: - '040' department: - _id: '7' doi: 10.5281/zenodo.8068466 editor: - first_name: Claus-Jochen full_name: Haake, Claus-Jochen last_name: Haake - first_name: Friedhelm full_name: Meyer auf der Heide, Friedhelm last_name: Meyer auf der Heide - 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 file: - access_level: open_access content_type: application/pdf creator: florida date_created: 2023-07-07T07:50:34Z date_updated: 2023-07-07T11:20:11Z file_id: '45885' file_name: B2-Chapter-SFB-Buch-Final.pdf file_size: 895091 relation: main_file file_date_updated: 2023-07-07T11:20:11Z has_accepted_license: '1' intvolume: ' 412' language: - iso: eng oa: '1' page: 85-104 place: Paderborn project: - _id: '1' grant_number: '160364472' name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' grant_number: '160364472' name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)' publication: On-The-Fly Computing -- Individualized IT-services in dynamic markets publisher: Heinz Nixdorf Institut, Universität Paderborn series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts status: public title: Configuration and Evaluation type: book_chapter user_id: '477' volume: 412 year: '2023' ... --- _id: '45863' abstract: - lang: eng text: "In the proposal for our CRC in 2011, we formulated a vision of markets for\r\nIT services that describes an approach to the provision of such services\r\nthat was novel at that time and, to a large extent, remains so today:\r\n„Our vision of on-the-fly computing is that of IT services individually and\r\nautomatically configured and brought to execution from flexibly combinable\r\nservices traded on markets. At the same time, we aim at organizing\r\nmarkets whose participants maintain a lively market of services through\r\nappropriate entrepreneurial actions.“\r\nOver the last 12 years, we have developed methods and techniques to\r\naddress problems critical to the convenient, efficient, and secure use of\r\non-the-fly computing. Among other things, we have made the description\r\nof services more convenient by allowing natural language input,\r\nincreased the quality of configured services through (natural language)\r\ninteraction and more efficient configuration processes and analysis\r\nprocedures, made the quality of (the products of) providers in the\r\nmarketplace transparent through reputation systems, and increased the\r\nresource efficiency of execution through reconfigurable heterogeneous\r\ncomputing nodes and an integrated treatment of service description and\r\nconfiguration. We have also developed network infrastructures that have\r\na high degree of adaptivity, scalability, efficiency, and reliability, and\r\nprovide cryptographic guarantees of anonymity and security for market\r\nparticipants and their products and services.\r\nTo demonstrate the pervasiveness of the OTF computing approach, we\r\nhave implemented a proof-of-concept for OTF computing that can run\r\ntypical scenarios of an OTF market. We illustrated the approach using\r\na cutting-edge application scenario – automated machine learning (AutoML).\r\nFinally, we have been pushing our work for the perpetuation of\r\nOn-The-Fly Computing beyond the SFB and sharing the expertise gained\r\nin the SFB in events with industry partners as well as transfer projects.\r\nThis work required a broad spectrum of expertise. Computer scientists\r\nand economists with research interests such as computer networks and\r\ndistributed algorithms, security and cryptography, software engineering\r\nand verification, configuration and machine learning, computer engineering\r\nand HPC, microeconomics and game theory, business informatics\r\nand management have successfully collaborated here." alternative_title: - Collaborative Research Centre 901 (2011 – 2023) author: - first_name: Claus-Jochen full_name: Haake, Claus-Jochen id: '20801' last_name: Haake - first_name: Friedhelm full_name: Meyer auf der Heide, Friedhelm id: '15523' last_name: Meyer auf der Heide - first_name: Marco full_name: Platzner, Marco id: '398' last_name: Platzner - first_name: Henning full_name: Wachsmuth, Henning id: '3900' last_name: Wachsmuth - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Heinz Nixdorf Institut, Universität Paderborn; 2023. doi:10.17619/UNIPB/1-1797 apa: Haake, C.-J., Meyer auf der Heide, F., Platzner, M., Wachsmuth, H., & Wehrheim, H. (2023). On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.17619/UNIPB/1-1797 bibtex: '@book{Haake_Meyer auf der Heide_Platzner_Wachsmuth_Wehrheim_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, volume={412}, DOI={10.17619/UNIPB/1-1797}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }' chicago: 'Haake, Claus-Jochen, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol. 412. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.17619/UNIPB/1-1797.' ieee: 'C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.' mla: Haake, Claus-Jochen, et al. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Heinz Nixdorf Institut, Universität Paderborn, 2023, doi:10.17619/UNIPB/1-1797. short: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim, On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023. date_created: 2023-07-05T07:16:51Z date_updated: 2023-08-29T06:44:36Z ddc: - '000' department: - _id: '7' doi: 10.17619/UNIPB/1-1797 file: - access_level: open_access content_type: application/pdf creator: ups date_created: 2023-07-05T07:15:55Z date_updated: 2023-07-05T07:19:14Z file_id: '45864' file_name: SFB-Buch-Final.pdf file_size: 15480050 relation: main_file file_date_updated: 2023-07-05T07:19:14Z has_accepted_license: '1' intvolume: ' 412' language: - iso: eng oa: '1' page: '247' place: Paderborn project: - _id: '1' grant_number: '160364472' name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ' - _id: '2' name: 'SFB 901 - A: SFB 901 - Project Area A' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '82' name: 'SFB 901 - T: SFB 901 - Project Area T' - _id: '5' grant_number: '160364472' name: 'SFB 901 - A1: SFB 901 - Möglichkeiten und Grenzen lokaler Strategien in dynamischen Netzen (Subproject A1)' - _id: '7' grant_number: '160364472' name: 'SFB 901 - A3: SFB 901 - Der Markt für Services: Anreize, Algorithmen, Implementation (Subproject A3)' - _id: '8' grant_number: '160364472' name: 'SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen (Subproject A4)' - _id: '9' grant_number: '160364472' name: 'SFB 901 - B1: SFB 901 - Parametrisierte Servicespezifikation (Subproject B1)' - _id: '10' grant_number: '160364472' name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)' - _id: '11' name: 'SFB 901 - B3: SFB 901 - Subproject B3' - _id: '12' name: 'SFB 901 - B4: SFB 901 - Subproject B4' - _id: '13' name: 'SFB 901 - C1: SFB 901 - Subproject C1' - _id: '14' grant_number: '160364472' name: 'SFB 901 - C2: SFB 901 - On-The-Fly Compute Centers I: Heterogene Ausführungsumgebungen (Subproject C2)' - _id: '16' grant_number: '160364472' name: 'SFB 901 - C4: SFB 901 - On-The-Fly Compute Centers II: Ausführung komponierter Dienste in konfigurierbaren Rechenzentren (Subproject C4)' - _id: '17' name: 'SFB 901 - C5: SFB 901 - Subproject C5' - _id: '83' name: 'SFB 901 - T1: SFB 901 -Subproject T1' - _id: '84' name: 'SFB 901 - T2: SFB 901 -Subproject T2' publication_identifier: unknown: - 978-3-947647-31-6 publisher: Heinz Nixdorf Institut, Universität Paderborn series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts status: public title: On-The-Fly Computing -- Individualized IT-services in dynamic markets type: book user_id: '477' volume: 412 year: '2023' ... --- _id: '30868' abstract: - lang: eng 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." author: - first_name: Elias full_name: Schede, Elias last_name: Schede - first_name: Jasmin full_name: Brandt, Jasmin last_name: Brandt - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Kevin full_name: Tierney, Kevin last_name: Tierney citation: 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. 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. ieee: E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022. mla: Schede, Elias, 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). date_created: 2022-04-12T12:00:08Z date_updated: 2022-04-12T12:01:15Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2202.01651' language: - iso: eng 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' publication: arXiv:2202.01651 status: public title: A Survey of Methods for Automated Algorithm Configuration type: preprint user_id: '38209' year: '2022' ... --- _id: '34103' abstract: - lang: eng text: "It is well known that different algorithms perform differently well on an\r\ninstance of an algorithmic problem, motivating algorithm selection (AS): Given\r\nan instance of an algorithmic problem, which is the most suitable algorithm to\r\nsolve it? As such, the AS problem has received considerable attention resulting\r\nin various approaches - many of which either solve a regression or ranking\r\nproblem under the hood. Although both of these formulations yield very natural\r\nways to tackle AS, they have considerable weaknesses. On the one hand,\r\ncorrectly predicting the performance of an algorithm on an instance is a\r\nsufficient, but not a necessary condition to produce a correct ranking over\r\nalgorithms and in particular ranking the best algorithm first. On the other\r\nhand, classical ranking approaches often do not account for concrete\r\nperformance values available in the training data, but only leverage rankings\r\ncomposed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon\r\nforeSts - a new algorithm selector leveraging special forests, combining the\r\nstrengths of both approaches while alleviating their weaknesses. HARRIS'\r\ndecisions are based on a forest model, whose trees are created based on splits\r\noptimized on a hybrid ranking and regression loss function. As our preliminary\r\nexperimental study on ASLib shows, HARRIS improves over standard algorithm\r\nselection approaches on some scenarios showing that combining ranking and\r\nregression in trees is indeed promising for AS." author: - first_name: Lukass full_name: Fehring, Lukass last_name: Fehring - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede citation: ama: 'Fehring L, Hanselle JM, Tornede A. HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022. ; 2022.' apa: 'Fehring, L., Hanselle, J. M., & Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore.' bibtex: '@inproceedings{Fehring_Hanselle_Tornede_2022, title={HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection}, booktitle={Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022}, author={Fehring, Lukass and Hanselle, Jonas Manuel and Tornede, Alexander}, year={2022} }' chicago: 'Fehring, Lukass, Jonas Manuel Hanselle, and Alexander Tornede. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” In Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.' ieee: 'L. Fehring, J. M. Hanselle, and A. Tornede, “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection,” presented at the Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore, 2022.' mla: 'Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.' short: 'L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.' conference: location: Baltimore name: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022 date_created: 2022-11-17T12:57:40Z date_updated: 2022-11-17T13:00:53Z external_id: arxiv: - '2210.17341' language: - iso: eng 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' publication: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022 status: public title: 'HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection' type: conference user_id: '38209' year: '2022' ... --- _id: '31806' abstract: - lang: eng text: The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds author: - first_name: Kevin full_name: Dreßler, Kevin id: '78256' last_name: Dreßler - first_name: Mohamed full_name: Sherif, Mohamed id: '67234' last_name: Sherif - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'Dreßler K, Sherif M, Ngonga Ngomo A-C. ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. In: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. ; 2022. doi:10.1145/3511095.3531287' apa: 'Dreßler, K., Sherif, M., & Ngonga Ngomo, A.-C. (2022). ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain). https://doi.org/10.1145/3511095.3531287' bibtex: '@inproceedings{Dreßler_Sherif_Ngonga Ngomo_2022, title={ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}, DOI={10.1145/3511095.3531287}, booktitle={Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}, author={Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2022} }' chicago: Dreßler, Kevin, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” In Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022. https://doi.org/10.1145/3511095.3531287. ieee: 'K. Dreßler, M. Sherif, and A.-C. Ngonga Ngomo, “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning,” presented at the HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain), 2022, doi: 10.1145/3511095.3531287.' mla: Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022, doi:10.1145/3511095.3531287. short: 'K. Dreßler, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022.' conference: end_date: 2022-07-01 location: Barcelona (Spain) name: 'HT ’22: 33rd ACM Conference on Hypertext and Social Media' start_date: 2022-06-28 date_created: 2022-06-08T08:47:33Z date_updated: 2022-11-18T10:11:38Z ddc: - '000' department: - _id: '34' doi: 10.1145/3511095.3531287 keyword: - 2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba language: - iso: eng 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' publication: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia status: public title: ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning type: conference user_id: '477' year: '2022' ... --- _id: '30867' 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." author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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. 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. 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. 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. short: A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022). date_created: 2022-04-12T11:58:56Z date_updated: 2022-08-24T12:44:27Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2109.06234' language: - iso: eng 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' publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence publisher: AAAI status: public title: Machine Learning for Online Algorithm Selection under Censored Feedback type: preprint user_id: '38209' year: '2022' ... --- _id: '30865' 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." author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Lukas full_name: Gehring, Lukas last_name: Gehring - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. Machine Learning. Published online 2022. apa: Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In Machine Learning. 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} }' 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. mla: Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine Learning, 2022. short: A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022). date_created: 2022-04-12T11:55:18Z date_updated: 2022-08-24T12:45:39Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2107.09414' language: - iso: eng 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' publication: Machine Learning status: public title: Algorithm Selection on a Meta Level type: preprint user_id: '38209' year: '2022' ... --- _id: '33090' 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.' author: - first_name: Karina full_name: Gevers, Karina id: '83151' last_name: Gevers - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Volker full_name: Schöppner, Volker id: '20530' last_name: Schöppner - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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 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 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. 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.' 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. short: K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022). date_created: 2022-08-24T12:51:07Z date_updated: 2022-08-24T12:52:06Z doi: 10.1007/s40194-022-01339-9 keyword: - Metals and Alloys - Mechanical Engineering - Mechanics of Materials language: - iso: eng 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' publication: Welding in the World publication_identifier: issn: - 0043-2288 - 1878-6669 publication_status: published publisher: Springer Science and Business Media LLC status: public title: A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials type: journal_article user_id: '38209' year: '2022' ... --- _id: '28350' abstract: - lang: eng text: "In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks).\r\nIn this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software\r\ndiscrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime" author: - first_name: Arnab full_name: Sharma, Arnab id: '67200' last_name: Sharma - first_name: Caglar full_name: Demir, Caglar id: '43817' last_name: Demir - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Sharma A, Demir C, Ngonga Ngomo A-C, Wehrheim H. MLCHECK–Property-Driven Testing of Machine Learning Classifiers. In: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE.' apa: Sharma, A., Demir, C., Ngonga Ngomo, A.-C., & Wehrheim, H. (n.d.). MLCHECK–Property-Driven Testing of Machine Learning Classifiers. Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA). bibtex: '@inproceedings{Sharma_Demir_Ngonga Ngomo_Wehrheim, title={MLCHECK–Property-Driven Testing of Machine Learning Classifiers}, booktitle={Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, publisher={IEEE}, author={Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike} }' chicago: Sharma, Arnab, Caglar Demir, Axel-Cyrille Ngonga Ngomo, and Heike Wehrheim. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” In Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, n.d. ieee: A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” mla: Sharma, Arnab, et al. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE. short: 'A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, in: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, n.d.' date_created: 2021-12-07T11:11:36Z date_updated: 2022-01-06T06:58:02Z department: - _id: '7' - _id: '77' - _id: '574' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 - _id: '10' name: SFB 901 - Subproject B2 publication: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA) publication_status: accepted publisher: IEEE status: public title: MLCHECK–Property-Driven Testing of Machine Learning Classifiers type: conference user_id: '477' year: '2021' ... --- _id: '21004' abstract: - lang: eng 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.' author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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' 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' 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} }' 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.' 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.' 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. date_created: 2021-01-16T14:48:13Z date_updated: 2022-01-06T06:54:42Z department: - _id: '34' - _id: '355' - _id: '26' doi: 10.1109/tpami.2021.3051276 keyword: - Automated Machine Learning - Multi Label Classification - Hierarchical Planning - Bayesian Optimization language: - iso: eng page: 1-1 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 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_identifier: issn: - 0162-8828 - 2160-9292 - 1939-3539 publication_status: published status: public title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation' type: journal_article user_id: '5786' year: '2021' ... --- _id: '21092' abstract: - lang: eng 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." author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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. 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. 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} }' 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. 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. 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. short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.). date_created: 2021-01-27T13:45:52Z date_updated: 2022-01-06T06:54:45Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_status: accepted publisher: IEEE status: public title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning type: journal_article user_id: '5786' year: '2021' ... --- _id: '21570' author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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. 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} }' 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. 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. 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. short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.' conference: end_date: 2021-07-14 name: Genetic and Evolutionary Computation Conference start_date: 2021-07-10 date_created: 2021-03-26T09:14:19Z date_updated: 2022-01-06T06:55:06Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 publication: Proceedings of the Genetic and Evolutionary Computation Conference status: public title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance type: conference user_id: '5786' year: '2021' ... --- _id: '22913' author: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 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). 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} }' chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021. 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. mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021. short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.' conference: end_date: 2021-09-17 location: Bilbao (Virtual) name: ECML/PKDD Workshop on Automating Data Science start_date: 2021-09-13 date_created: 2021-08-02T07:46:29Z date_updated: 2022-01-06T06:55:43Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 quality_controlled: '1' status: public title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning type: conference user_id: '5786' year: '2021' ... --- _id: '30866' abstract: - lang: eng text: "Automated machine learning (AutoML) strives for the automatic configuration\r\nof machine learning algorithms and their composition into an overall (software)\r\nsolution - a machine learning pipeline - tailored to the learning task\r\n(dataset) at hand. Over the last decade, AutoML has developed into an\r\nindependent research field with hundreds of contributions. While AutoML offers\r\nmany prospects, it is also known to be quite resource-intensive, which is one\r\nof its major points of criticism. The primary cause for a high resource\r\nconsumption is that many approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines while searching for good candidates. This problem is\r\namplified in the context of research on AutoML methods, due to large scale\r\nexperiments conducted with many datasets and approaches, each of them being run\r\nwith several repetitions to rule out random effects. In the spirit of recent\r\nwork on Green AI, this paper is written in an attempt to raise the awareness of\r\nAutoML researchers for the problem and to elaborate on possible remedies. To\r\nthis end, we identify four categories of actions the community may take towards\r\nmore sustainable research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking, transparency and research incentives." author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850. Published online 2021.' apa: 'Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions. In arXiv:2111.05850.' bibtex: '@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850}, author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }' chicago: 'Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.' ieee: 'T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” arXiv:2111.05850. 2021.' mla: 'Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.' short: T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier, ArXiv:2111.05850 (2021). date_created: 2022-04-12T11:57:15Z date_updated: 2022-04-12T12:01:23Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2111.05850' language: - iso: eng 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' publication: arXiv:2111.05850 status: public title: 'Towards Green Automated Machine Learning: Status Quo and Future Directions' type: preprint user_id: '38209' year: '2021' ... --- _id: '21198' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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} }' 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.' 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.' 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). conference: end_date: 2021-05-14 location: Delhi, India name: The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021) start_date: 2021-05-11 date_created: 2021-02-09T09:30:14Z date_updated: 2022-08-24T12:49:06Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 series_title: PAKDD status: public title: 'Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data' type: conference user_id: '38209' year: '2021' ... --- _id: '17407' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.' apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. 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} }' chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” 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. mla: Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020. short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.' conference: name: Discovery Science 2020 date_created: 2020-07-21T10:06:51Z date_updated: 2022-01-06T06:53:10Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 publication: Discovery Science status: public title: Extreme Algorithm Selection with Dyadic Feature Representation type: conference user_id: '5786' year: '2020' ... --- _id: '17408' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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.' 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} }' 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.' 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. mla: 'Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.' short: 'J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 2020.' conference: name: 43rd German Conference on Artificial Intelligence date_created: 2020-07-21T10:21:09Z date_updated: 2022-01-06T06:53:10Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 publication: 'KI 2020: Advances in Artificial Intelligence' status: public title: Hybrid Ranking and Regression for Algorithm Selection type: conference user_id: '5786' year: '2020' ... --- _id: '17424' author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020. ; 2020. doi:10.1007/978-3-030-66770-2_8' apa: 'Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8' bibtex: '@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }' chicago: 'Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.' ieee: 'T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.' mla: 'Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.' short: 'T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.' conference: name: IOTStream Workshop @ ECMLPKDD 2020 date_created: 2020-07-28T09:17:41Z date_updated: 2022-01-06T06:53:11Z department: - _id: '34' - _id: '355' - _id: '26' doi: 10.1007/978-3-030-66770-2_8 language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '1' name: SFB 901 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proceedings of the ECMLPKDD 2020 status: public title: 'AutoML for Predictive Maintenance: One Tool to RUL Them All' type: conference user_id: '5786' year: '2020' ... --- _id: '20306' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.' apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online. bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }' chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020. ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020. mla: Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020. short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.' conference: location: Online name: Workshop MetaLearn 2020 @ NeurIPS 2020 date_created: 2020-11-06T09:42:27Z date_updated: 2022-01-06T06:54:26Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng 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 publication: Workshop MetaLearn 2020 @ NeurIPS 2020 status: public title: Towards Meta-Algorithm Selection type: conference user_id: '5786' year: '2020' ... --- _id: '18276' abstract: - lang: eng text: "Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom a fixed set of candidate algorithms most suitable for a specific instance\r\nof an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training data for algorithm selection models is usually generated\r\nunder time constraints in the sense that not all algorithms are run to\r\ncompletion on all instances. Thus, training data usually comprises censored\r\ninformation, as the true runtime of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches are not able to handle such information in\r\na proper way. On the other side, survival analysis (SA) naturally supports\r\ncensored data and offers appropriate ways to use such data for learning\r\ndistributional models of algorithm runtime, as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive. Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse approach to algorithm\r\nselection, in which the avoidance of a timeout is given high priority. In an\r\nextensive experimental study with the standard benchmark ASlib, our approach is\r\nshown to be highly competitive and in many cases even superior to\r\nstate-of-the-art AS approaches." author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.' apa: 'Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.' bibtex: '@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}, booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }' chicago: 'Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In ACML 2020, 2020.' ieee: 'A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.' mla: 'Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.' short: 'A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.' conference: end_date: 2020-11-20 location: Bangkok, Thailand name: 12th Asian Conference on Machine Learning start_date: 2020-11-18 date_created: 2020-08-25T12:09:28Z date_updated: 2022-01-06T06:53:28Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - url: https://arxiv.org/pdf/2007.02816.pdf 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 publication: ACML 2020 status: public title: 'Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis' type: conference user_id: '5786' year: '2020' ...