--- _id: '10181' author: - first_name: Vu-Linh full_name: Nguyen, Vu-Linh last_name: Nguyen - first_name: Sebastian full_name: Destercke, Sebastian last_name: Destercke - first_name: M.-H. full_name: Masson, M.-H. last_name: Masson - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Nguyen V-L, Destercke S, Masson M-H, Hüllermeier E. Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. In: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI). ; 2018:5089-5095.' apa: Nguyen, V.-L., Destercke, S., Masson, M.-H., & Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–5095. bibtex: '@inproceedings{Nguyen_Destercke_Masson_Hüllermeier_2018, title={Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty}, booktitle={Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI)}, author={Nguyen, Vu-Linh and Destercke, Sebastian and Masson, M.-H. and Hüllermeier, Eyke}, year={2018}, pages={5089–5095} }' chicago: Nguyen, Vu-Linh, Sebastian Destercke, M.-H. Masson, and Eyke Hüllermeier. “Reliable Multi-Class Classification Based on Pairwise Epistemic and Aleatoric Uncertainty.” In Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–95, 2018. ieee: V.-L. Nguyen, S. Destercke, M.-H. Masson, and E. Hüllermeier, “Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty,” in Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095. mla: Nguyen, Vu-Linh, et al. “Reliable Multi-Class Classification Based on Pairwise Epistemic and Aleatoric Uncertainty.” Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–95. short: 'V.-L. Nguyen, S. Destercke, M.-H. Masson, E. Hüllermeier, in: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095.' date_created: 2019-06-07T12:31:20Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' - _id: '26' language: - iso: eng page: 5089-5095 publication: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI) status: public title: Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty type: conference user_id: '5786' year: '2018' ... --- _id: '10184' author: - first_name: Dirk full_name: Schäfer, Dirk last_name: Schäfer - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Proc. 21st Int. Conference on Discovery Science (DS). ; 2018:161-175.' apa: Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. Proc. 21st Int. Conference on Discovery Science (DS), 161–175. bibtex: '@inproceedings{Schäfer_Hüllermeier_2018, title={Preference-Based Reinforcement Learning Using Dyad Ranking}, booktitle={Proc. 21st Int. Conference on Discovery Science (DS)}, author={Schäfer, Dirk and Hüllermeier, Eyke}, year={2018}, pages={161–175} }' chicago: Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In Proc. 21st Int. Conference on Discovery Science (DS), 161–75, 2018. ieee: D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175. mla: Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–75. short: 'D. Schäfer, E. Hüllermeier, in: Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175.' date_created: 2019-06-07T12:33:58Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' - _id: '26' language: - iso: eng page: 161-175 publication: Proc. 21st Int. Conference on Discovery Science (DS) status: public title: Preference-Based Reinforcement Learning Using Dyad Ranking type: conference user_id: '5786' year: '2018' ... --- _id: '10276' author: - first_name: Dirk full_name: Schäfer, Dirk last_name: Schäfer - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Schäfer D, Hüllermeier E. Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning. 2018;107(5):903-941. apa: Schäfer, D., & Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941. bibtex: '@article{Schäfer_Hüllermeier_2018, title={Dyad Ranking Using Plackett-Luce Models based on joint feature representations}, volume={107}, number={5}, journal={Machine Learning}, author={Schäfer, Dirk and Hüllermeier, Eyke}, year={2018}, pages={903–941} }' chicago: 'Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018): 903–41.' ieee: D. Schäfer and E. Hüllermeier, “Dyad Ranking Using Plackett-Luce Models based on joint feature representations,” Machine Learning, vol. 107, no. 5, pp. 903–941, 2018. mla: Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning, vol. 107, no. 5, 2018, pp. 903–41. short: D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941. date_created: 2019-06-19T14:58:10Z date_updated: 2022-01-06T06:50:33Z department: - _id: '34' - _id: '7' - _id: '355' - _id: '26' intvolume: ' 107' issue: '5' language: - iso: eng page: 903-941 publication: Machine Learning status: public title: Dyad Ranking Using Plackett-Luce Models based on joint feature representations type: journal_article user_id: '49109' volume: 107 year: '2018' ... --- _id: '1379' author: - first_name: Nina full_name: Seemann, Nina id: '65408' last_name: Seemann - first_name: Michaela full_name: Geierhos, Michaela id: '42496' last_name: Geierhos orcid: 0000-0002-8180-5606 - first_name: Marie-Luis full_name: Merten, Marie-Luis last_name: Merten - first_name: Doris full_name: Tophinke, Doris id: '16277' last_name: Tophinke - 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: 'Seemann N, Geierhos M, Merten M-L, Tophinke D, Wever MD, Hüllermeier E. Supporting the Cognitive Process in Annotation Tasks. In: Eckart K, Schlechtweg D, eds. Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. ; 2018.' apa: Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., & Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart & D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. bibtex: '@inproceedings{Seemann_Geierhos_Merten_Tophinke_Wever_Hüllermeier_2018, title={Supporting the Cognitive Process in Annotation Tasks}, booktitle={Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft}, author={Seemann, Nina and Geierhos, Michaela and Merten, Marie-Luis and Tophinke, Doris and Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Eckart, Kerstin and Schlechtweg, Dominik }, year={2018} }' chicago: Seemann, Nina, Michaela Geierhos, Marie-Luis Merten, Doris Tophinke, Marcel Dominik Wever, and Eyke Hüllermeier. “Supporting the Cognitive Process in Annotation Tasks.” In Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, edited by Kerstin Eckart and Dominik Schlechtweg, 2018. ieee: N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M. D. Wever, and E. Hüllermeier, “Supporting the Cognitive Process in Annotation Tasks,” in Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, Stuttgart, Germany, 2018. mla: Seemann, Nina, et al. “Supporting the Cognitive Process in Annotation Tasks.” Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, edited by Kerstin Eckart and Dominik Schlechtweg, 2018. short: 'N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M.D. Wever, E. Hüllermeier, in: K. Eckart, D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, 2018.' conference: end_date: 2018-03-09 location: Stuttgart, Germany name: Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft start_date: 2018-03-07 date_created: 2018-03-19T15:23:25Z date_updated: 2023-01-09T14:56:56Z ddc: - '410' department: - _id: '36' - _id: '1' - _id: '579' - _id: '115' - _id: '355' - _id: '115' editor: - first_name: 'Kerstin ' full_name: 'Eckart, Kerstin ' last_name: Eckart - first_name: 'Dominik ' full_name: 'Schlechtweg, Dominik ' last_name: Schlechtweg file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:32:38Z date_updated: 2018-11-06T15:32:38Z file_id: '5389' file_name: 2018_dgfs-cl-poster-seemann-etal.pdf file_size: 158928 relation: main_file success: 1 file_date_updated: 2018-11-06T15:32:38Z has_accepted_license: '1' language: - iso: ger main_file_link: - open_access: '1' url: https://www.dgfs2018.uni-stuttgart.de/programm/postersession/programm-cl-postersession/2018_dgfs-cl-poster-seemann-etal.pdf oa: '1' project: - _id: '39' name: InterGramm publication: Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft publication_status: published quality_controlled: '1' status: public title: Supporting the Cognitive Process in Annotation Tasks type: conference_abstract user_id: '16277' year: '2018' ... --- _id: '22996' abstract: - lang: eng text: The effective control design of a dynamical system traditionally relies on a high level of system understanding, usually expressed in terms of an exact physical model. In contrast to this, reinforcement learning adopts a data-driven approach and constructs an optimal control strategy by interacting with the underlying system. To keep the wear of real-world systems as low as possible, the learning process should be short. In our research, we used the state-of-the-art reinforcement learning method PILCO to design a feedback control strategy for the swing-up of the double pendulum on a cart with remarkably few test iterations at the test bench. PILCO stands for “probabilistic inference for learning control” and requires only few expert knowledge for learning. To achieve the swing-up of a double pendulum on a cart to its upper unstable equilibrium position, we introduce additional state restrictions to PILCO, so that the limited cart distance can be taken into account. Thanks to these measures, we were able to learn the swing up at the real test bench for the first time and in only 27 learning iterations. author: - first_name: Michael full_name: Hesse, Michael id: '29222' last_name: Hesse - first_name: Julia full_name: Timmermann, Julia id: '15402' last_name: Timmermann - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Ansgar full_name: Trächtler, Ansgar id: '552' last_name: Trächtler citation: ama: Hesse M, Timmermann J, Hüllermeier E, Trächtler A. A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Procedia Manufacturing. 2018;24:15-20. apa: Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Procedia Manufacturing, 24, 15–20. bibtex: '@article{Hesse_Timmermann_Hüllermeier_Trächtler_2018, title={A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart}, volume={24}, journal={Procedia Manufacturing}, author={Hesse, Michael and Timmermann, Julia and Hüllermeier, Eyke and Trächtler, Ansgar}, year={2018}, pages={15–20} }' chicago: 'Hesse, Michael, Julia Timmermann, Eyke Hüllermeier, and Ansgar Trächtler. “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.” Procedia Manufacturing 24 (2018): 15–20.' ieee: M. Hesse, J. Timmermann, E. Hüllermeier, and A. Trächtler, “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart,” Procedia Manufacturing, vol. 24, pp. 15–20, 2018. mla: Hesse, Michael, et al. “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.” Procedia Manufacturing, vol. 24, 2018, pp. 15–20. short: M. Hesse, J. Timmermann, E. Hüllermeier, A. Trächtler, Procedia Manufacturing 24 (2018) 15–20. date_created: 2021-08-09T05:41:38Z date_updated: 2023-11-06T15:17:24Z department: - _id: '153' intvolume: ' 24' language: - iso: eng page: 15 - 20 publication: Procedia Manufacturing quality_controlled: '1' status: public title: A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart type: journal_article user_id: '29222' volume: 24 year: '2018' ... --- _id: '3325' author: - first_name: Vitalik full_name: Melnikov, Vitalik last_name: Melnikov - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Melnikov V, Hüllermeier E. Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing; 2017. doi:10.5445/KSP/1000074341' apa: 'Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000074341' bibtex: '@inproceedings{Melnikov_Hüllermeier_2017, title={Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics}, DOI={10.5445/KSP/1000074341}, booktitle={Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017}, publisher={KIT Scientific Publishing}, author={Melnikov, Vitalik and Hüllermeier, Eyke}, year={2017} }' chicago: 'Melnikov, Vitalik, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics.” In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing, 2017. https://doi.org/10.5445/KSP/1000074341.' ieee: 'V. Melnikov and E. Hüllermeier, “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics,” in Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, 2017.' mla: 'Melnikov, Vitalik, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics.” Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, KIT Scientific Publishing, 2017, doi:10.5445/KSP/1000074341.' short: 'V. Melnikov, E. Hüllermeier, in: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, KIT Scientific Publishing, 2017.' date_created: 2018-06-25T08:14:49Z date_updated: 2022-01-06T06:59:10Z ddc: - '000' department: - _id: '355' doi: 10.5445/KSP/1000074341 file: - access_level: closed content_type: application/pdf creator: melnikov date_created: 2018-11-30T09:47:59Z date_updated: 2018-11-30T09:47:59Z file_id: '5987' file_name: main.pdf file_size: 1829552 relation: main_file success: 1 file_date_updated: 2018-11-30T09:47:59Z has_accepted_license: '1' language: - iso: eng project: - _id: '11' name: SFB 901 - Subproject B3 - _id: '3' name: SFB 901 - Project Area B - _id: '1' name: SFB 901 publication: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017 publisher: KIT Scientific Publishing status: public title: 'Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics' type: conference user_id: '15504' year: '2017' ... --- _id: '71' abstract: - lang: eng text: Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6). author: - first_name: Mike full_name: Czech, Mike last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Tools. In: Proceedings of the 3rd International Workshop on Software Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262' apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262 bibtex: '@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, series={SWAN’17}, title={Predicting Rankings of Software Verification Tools}, DOI={10.1145/3121257.3121262}, booktitle={Proceedings of the 3rd International Workshop on Software Analytics}, author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017}, pages={23–26}, collection={SWAN’17} }' chicago: Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In Proceedings of the 3rd International Workshop on Software Analytics, 23–26. SWAN’17, 2017. https://doi.org/10.1145/3121257.3121262. ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings of Software Verification Tools,” in Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26. mla: Czech, Mike, et al. “Predicting Rankings of Software Verification Tools.” Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26, doi:10.1145/3121257.3121262. short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.' date_created: 2017-10-17T12:41:05Z date_updated: 2022-01-06T07:03:28Z ddc: - '000' department: - _id: '355' - _id: '77' doi: 10.1145/3121257.3121262 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T14:24:29Z date_updated: 2018-11-02T14:24:29Z file_id: '5271' file_name: fsews17swan-swanmain1.pdf file_size: 822383 relation: main_file success: 1 file_date_updated: 2018-11-02T14:24:29Z has_accepted_license: '1' language: - iso: eng page: 23-26 project: - _id: '1' name: SFB 901 - _id: '12' name: SFB 901 - Subprojekt B4 - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 publication: Proceedings of the 3rd International Workshop on Software Analytics series_title: SWAN'17 status: public title: Predicting Rankings of Software Verification Tools type: conference user_id: '15504' year: '2017' ... --- _id: '72' abstract: - lang: eng text: 'Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency. Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of verification tools on a given verification task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on verification tasks. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). ' author: - first_name: Mike full_name: Czech, Mike last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Competitions.; 2017. apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions. bibtex: '@book{Czech_Hüllermeier_Jakobs_Wehrheim_2017, title={Predicting Rankings of Software Verification Competitions}, author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017} }' chicago: Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. Predicting Rankings of Software Verification Competitions, 2017. ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, Predicting Rankings of Software Verification Competitions. 2017. mla: Czech, Mike, et al. Predicting Rankings of Software Verification Competitions. 2017. short: M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings of Software Verification Competitions, 2017. date_created: 2017-10-17T12:41:05Z date_updated: 2022-01-06T07:03:29Z ddc: - '000' department: - _id: '77' - _id: '355' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-11-21T10:50:11Z date_updated: 2018-11-21T10:50:11Z file_id: '5782' file_name: "Predicting Rankings of So\x81ware Verification Competitions.pdf" file_size: 869984 relation: main_file success: 1 file_date_updated: 2018-11-21T10:50:11Z has_accepted_license: '1' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '11' name: SFB 901 - Subprojekt B3 - _id: '12' name: SFB 901 - Subprojekt B4 - _id: '3' name: SFB 901 - Project Area B status: public title: Predicting Rankings of Software Verification Competitions type: report user_id: '15504' year: '2017' ... --- _id: '10589' author: - first_name: J. full_name: Fürnkranz, J. last_name: Fürnkranz - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Fürnkranz J, Hüllermeier E. Preference Learning. In: Encyclopedia of Machine Learning and Data Mining. ; 2017:1000-1005.' apa: Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005). bibtex: '@inbook{Fürnkranz_Hüllermeier_2017, title={Preference Learning}, booktitle={Encyclopedia of Machine Learning and Data Mining}, author={Fürnkranz, J. and Hüllermeier, Eyke}, year={2017}, pages={1000–1005} }' chicago: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, 1000–1005, 2017. ieee: J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005. mla: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–05. short: 'J. Fürnkranz, E. Hüllermeier, in: Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.' date_created: 2019-07-09T15:37:09Z date_updated: 2022-01-06T06:50:45Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 1000-1005 publication: Encyclopedia of Machine Learning and Data Mining status: public title: Preference Learning type: encyclopedia_article user_id: '49109' year: '2017' ... --- _id: '10784' author: - first_name: J. full_name: Fürnkranz, J. last_name: Fürnkranz - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Vol 107. Springer; 2017:1000-1005.' apa: Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer. bibtex: '@inbook{Fürnkranz_Hüllermeier_2017, title={Preference Learning}, volume={107}, booktitle={Encyclopedia of Machine Learning and Data Mining}, publisher={Springer}, author={Fürnkranz, J. and Hüllermeier, Eyke}, editor={Sammut, C. and Webb, G.I.Editors}, year={2017}, pages={1000–1005} }' chicago: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, 107:1000–1005. Springer, 2017. ieee: J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, vol. 107, C. Sammut and G. I. Webb, Eds. Springer, 2017, pp. 1000–1005. mla: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, vol. 107, Springer, 2017, pp. 1000–05. short: 'J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2017, pp. 1000–1005.' date_created: 2019-07-10T15:44:32Z date_updated: 2022-01-06T06:50:50Z department: - _id: '34' - _id: '7' - _id: '355' editor: - first_name: C. full_name: Sammut, C. last_name: Sammut - first_name: G.I. full_name: Webb, G.I. last_name: Webb intvolume: ' 107' language: - iso: eng page: 1000-1005 publication: Encyclopedia of Machine Learning and Data Mining publisher: Springer status: public title: Preference Learning type: book_chapter user_id: '49109' volume: 107 year: '2017' ... --- _id: '1180' abstract: - lang: eng text: These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings. 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: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence. Dortmund; 2017.' apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.' bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2017} }' chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop Computational Intelligence. Dortmund, 2017.' ieee: 'M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.' mla: 'Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence, 2017.' short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.' conference: end_date: 2017-11-24 location: Dortmund name: 27th Workshop Computational Intelligence start_date: 2017-11-23 date_created: 2018-02-22T07:19:18Z date_updated: 2022-01-06T06:51:09Z ddc: - '000' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:28:09Z date_updated: 2018-11-06T15:28:09Z file_id: '5387' file_name: CI Workshop AutoML.pdf file_size: 323589 relation: main_file success: 1 file_date_updated: 2018-11-06T15:28:09Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://publikationen.bibliothek.kit.edu/1000074341/4643874 oa: '1' place: Dortmund project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: 27th Workshop Computational Intelligence publication_status: published status: public title: 'Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization' type: conference user_id: '49109' year: '2017' ... --- _id: '15397' author: - first_name: Vitaly full_name: Melnikov, Vitaly last_name: Melnikov - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Melnikov V, Hüllermeier E. Optimizing the structure of nested dichotomies. A comparison of two heuristics. In: Hoffmann F, Hüllermeier E, Mikut R, eds. In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2017:1-12.' apa: Melnikov, V., & Hüllermeier, E. (2017). Optimizing the structure of nested dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany (pp. 1–12). KIT Scientific Publishing. bibtex: '@inproceedings{Melnikov_Hüllermeier_2017, title={Optimizing the structure of nested dichotomies. A comparison of two heuristics}, booktitle={in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany}, publisher={KIT Scientific Publishing}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.Editors}, year={2017}, pages={1–12} }' chicago: Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies. A Comparison of Two Heuristics.” In In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann, Eyke Hüllermeier, and R. Mikut, 1–12. KIT Scientific Publishing, 2017. ieee: V. Melnikov and E. Hüllermeier, “Optimizing the structure of nested dichotomies. A comparison of two heuristics,” in in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, 2017, pp. 1–12. mla: Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies. A Comparison of Two Heuristics.” In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann et al., KIT Scientific Publishing, 2017, pp. 1–12. short: 'V. Melnikov, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2017, pp. 1–12.' date_created: 2019-12-19T15:48:38Z date_updated: 2022-01-06T06:52:22Z department: - _id: '34' - _id: '7' - _id: '355' editor: - first_name: F. full_name: Hoffmann, F. last_name: Hoffmann - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier - first_name: R. full_name: Mikut, R. last_name: Mikut language: - iso: eng page: 1-12 publication: in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany publisher: KIT Scientific Publishing status: public title: Optimizing the structure of nested dichotomies. A comparison of two heuristics type: conference user_id: '49109' year: '2017' ... --- _id: '15399' author: - first_name: M. full_name: Czech, M. last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: M.C. full_name: Jacobs, M.C. last_name: Jacobs - first_name: Heike full_name: Wehrheim, Heike last_name: Wehrheim citation: ama: 'Czech M, Hüllermeier E, Jacobs MC, Wehrheim H. Predicting rankings of software verification tools. In: In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany. ; 2017.' apa: Czech, M., Hüllermeier, E., Jacobs, M. C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany. bibtex: '@inproceedings{Czech_Hüllermeier_Jacobs_Wehrheim_2017, title={Predicting rankings of software verification tools}, booktitle={in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany}, author={Czech, M. and Hüllermeier, Eyke and Jacobs, M.C. and Wehrheim, Heike}, year={2017} }' chicago: Czech, M., Eyke Hüllermeier, M.C. Jacobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017. ieee: M. Czech, E. Hüllermeier, M. C. Jacobs, and H. Wehrheim, “Predicting rankings of software verification tools,” in in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017. mla: Czech, M., et al. “Predicting Rankings of Software Verification Tools.” In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017. short: 'M. Czech, E. Hüllermeier, M.C. Jacobs, H. Wehrheim, in: In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017.' date_created: 2019-12-19T15:59:42Z date_updated: 2022-01-06T06:52:22Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng publication: in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany status: public title: Predicting rankings of software verification tools type: conference user_id: '49109' year: '2017' ... --- _id: '15110' author: - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: D. full_name: Dubois, D. last_name: Dubois - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Couso I, Dubois D, Hüllermeier E. Maximum likelihood estimation and coarse data. In: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain. Springer; 2017:3-16.' apa: Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data. In in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer. bibtex: '@inproceedings{Couso_Dubois_Hüllermeier_2017, title={Maximum likelihood estimation and coarse data}, booktitle={in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain}, publisher={Springer}, author={Couso, Ines and Dubois, D. and Hüllermeier, Eyke}, year={2017}, pages={3–16} }' chicago: Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation and Coarse Data.” In In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, 3–16. Springer, 2017. ieee: I. Couso, D. Dubois, and E. Hüllermeier, “Maximum likelihood estimation and coarse data,” in in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, 2017, pp. 3–16. mla: Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017, pp. 3–16. short: 'I. Couso, D. Dubois, E. Hüllermeier, in: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017, pp. 3–16.' date_created: 2019-11-21T16:38:39Z date_updated: 2022-01-06T06:52:15Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 3-16 publication: in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain publisher: Springer status: public title: Maximum likelihood estimation and coarse data type: conference user_id: '49109' year: '2017' ... --- _id: '10204' author: - first_name: Ralph full_name: Ewerth, Ralph last_name: Ewerth - first_name: M. full_name: Springstein, M. last_name: Springstein - first_name: E. full_name: Müller, E. last_name: Müller - first_name: A. full_name: Balz, A. last_name: Balz - first_name: J. full_name: Gehlhaar, J. last_name: Gehlhaar - first_name: T. full_name: Naziyok, T. last_name: Naziyok - first_name: K. full_name: Dembczynski, K. last_name: Dembczynski - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Ewerth R, Springstein M, Müller E, et al. Estimating relative depth in single images via rankboost. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017). ; 2017:919-924.' apa: Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T., … Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924). bibtex: '@inproceedings{Ewerth_Springstein_Müller_Balz_Gehlhaar_Naziyok_Dembczynski_Hüllermeier_2017, title={Estimating relative depth in single images via rankboost}, booktitle={Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017)}, author={Ewerth, Ralph and Springstein, M. and Müller, E. and Balz, A. and Gehlhaar, J. and Naziyok, T. and Dembczynski, K. and Hüllermeier, Eyke}, year={2017}, pages={919–924} }' chicago: Ewerth, Ralph, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok, K. Dembczynski, and Eyke Hüllermeier. “Estimating Relative Depth in Single Images via Rankboost.” In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 919–24, 2017. ieee: R. Ewerth et al., “Estimating relative depth in single images via rankboost,” in Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924. mla: Ewerth, Ralph, et al. “Estimating Relative Depth in Single Images via Rankboost.” Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–24. short: 'R. Ewerth, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok, K. Dembczynski, E. Hüllermeier, in: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924.' date_created: 2019-06-07T15:18:24Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 919-924 publication: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) status: public title: Estimating relative depth in single images via rankboost type: conference user_id: '49109' year: '2017' ... --- _id: '10205' author: - first_name: Mohsen full_name: Ahmadi Fahandar, Mohsen last_name: Ahmadi Fahandar - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Ines full_name: Couso, Ines last_name: Couso citation: ama: 'Ahmadi Fahandar M, Hüllermeier E, Couso I. Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In: Proc. 34th Int. Conf. on Machine Learning (ICML 2017). ; 2017:1078-1087.' apa: 'Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).' bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_Couso_2017, title={Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening}, booktitle={Proc. 34th Int. Conf. on Machine Learning (ICML 2017)}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke and Couso, Ines}, year={2017}, pages={1078–1087} }' chicago: 'Ahmadi Fahandar, Mohsen, Eyke Hüllermeier, and Ines Couso. “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening.” In Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 1078–87, 2017.' ieee: 'M. Ahmadi Fahandar, E. Hüllermeier, and I. Couso, “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening,” in Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.' mla: 'Ahmadi Fahandar, Mohsen, et al. “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening.” Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–87.' short: 'M. Ahmadi Fahandar, E. Hüllermeier, I. Couso, in: Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.' date_created: 2019-06-07T15:22:01Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 1078-1087 publication: Proc. 34th Int. Conf. on Machine Learning (ICML 2017) status: public title: 'Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening' type: conference user_id: '49109' year: '2017' ... --- _id: '10206' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Theodor full_name: Lettmann, Theodor id: '315' last_name: Lettmann orcid: 0000-0001-5859-2457 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Lettmann T, Hüllermeier E. Planning with Independent Task Networks. In: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017). ; 2017:193-206. doi:10.1007/978-3-319-67190-1_15' apa: Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206). https://doi.org/10.1007/978-3-319-67190-1_15 bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_2017, title={Planning with Independent Task Networks}, DOI={10.1007/978-3-319-67190-1_15}, booktitle={Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017)}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke}, year={2017}, pages={193–206} }' chicago: Mohr, Felix, Theodor Lettmann, and Eyke Hüllermeier. “Planning with Independent Task Networks.” In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 193–206, 2017. https://doi.org/10.1007/978-3-319-67190-1_15. ieee: F. Mohr, T. Lettmann, and E. Hüllermeier, “Planning with Independent Task Networks,” in Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206. mla: Mohr, Felix, et al. “Planning with Independent Task Networks.” Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206, doi:10.1007/978-3-319-67190-1_15. short: 'F. Mohr, T. Lettmann, E. Hüllermeier, in: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206.' date_created: 2019-06-07T15:24:16Z date_updated: 2022-01-06T06:50:31Z ddc: - '000' department: - _id: '7' - _id: '34' - _id: '355' doi: 10.1007/978-3-319-67190-1_15 file: - access_level: open_access content_type: application/pdf creator: lettmann date_created: 2020-02-28T12:50:18Z date_updated: 2020-02-28T12:50:18Z file_id: '16157' file_name: ki17.pdf file_size: 374421 relation: main_file file_date_updated: 2020-02-28T12:50:18Z has_accepted_license: '1' language: - iso: eng oa: '1' page: 193-206 publication: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) status: public title: Planning with Independent Task Networks type: conference user_id: '315' year: '2017' ... --- _id: '10207' author: - first_name: M. full_name: Czech, M. last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: M.-C. full_name: Jakobs, M.-C. last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting rankings of software verification tools. In: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017. ; 2017:23-26.' apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26). bibtex: '@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, title={Predicting rankings of software verification tools}, booktitle={Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017}, author={Czech, M. and Hüllermeier, Eyke and Jakobs, M.-C. and Wehrheim, Heike}, year={2017}, pages={23–26} }' chicago: Czech, M., Eyke Hüllermeier, M.-C. Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 23–26, 2017. ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting rankings of software verification tools,” in Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26. mla: Czech, M., et al. “Predicting Rankings of Software Verification Tools.” Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26. short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.' date_created: 2019-06-07T15:27:47Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 23-26 publication: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 status: public title: Predicting rankings of software verification tools type: conference user_id: '49109' year: '2017' ... --- _id: '10208' author: - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: D. full_name: Dubois, D. last_name: Dubois - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Couso I, Dubois D, Hüllermeier E. Maximum Likelihood Estimation and Coarse Data. In: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017). ; 2017:3-16.' apa: Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16). bibtex: '@inproceedings{Couso_Dubois_Hüllermeier_2017, title={Maximum Likelihood Estimation and Coarse Data}, booktitle={Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017)}, author={Couso, Ines and Dubois, D. and Hüllermeier, Eyke}, year={2017}, pages={3–16} }' chicago: Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation and Coarse Data.” In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 3–16, 2017. ieee: I. Couso, D. Dubois, and E. Hüllermeier, “Maximum Likelihood Estimation and Coarse Data,” in Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16. mla: Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16. short: 'I. Couso, D. Dubois, E. Hüllermeier, in: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16.' date_created: 2019-06-07T15:30:48Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 3-16 publication: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) status: public title: Maximum Likelihood Estimation and Coarse Data type: conference user_id: '49109' year: '2017' ... --- _id: '10209' author: - first_name: Mohsen full_name: Ahmadi Fahandar, Mohsen last_name: Ahmadi Fahandar - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Ahmadi Fahandar M, Hüllermeier E. Learning to Rank based on Analogical Reasoning. In: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence. ; 2017.' apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence. bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_2017, title={Learning to Rank based on Analogical Reasoning}, booktitle={Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2017} }' chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017. ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank based on Analogical Reasoning,” in Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017. mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017. short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017.' date_created: 2019-06-07T15:33:14Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng publication: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence status: public title: Learning to Rank based on Analogical Reasoning type: conference user_id: '49109' year: '2017' ...