--- _id: '2479' 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: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Amin full_name: Faez, Amin last_name: Faez citation: ama: 'Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039' apa: 'Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039' bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco, CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services}, DOI={10.1109/SCC.2018.00039}, booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke and Faez, Amin}, year={2018} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.” In SCC. San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.' ieee: F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018. mla: Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039. short: 'F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018.' conference: end_date: 2018-07-07 location: San Francisco, CA, USA name: IEEE International Conference on Services Computing, SCC 2018 start_date: 2018-07-02 date_created: 2018-04-24T08:34:52Z date_updated: 2022-01-06T06:56:35Z ddc: - '000' department: - _id: '355' doi: 10.1109/SCC.2018.00039 file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:08:39Z date_updated: 2018-11-06T15:08:39Z file_id: '5382' file_name: 08456425.pdf file_size: 237890 relation: main_file file_date_updated: 2018-11-06T15:08:39Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://ieeexplore.ieee.org/document/8456425 oa: '1' place: San Francisco, CA, USA project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: SCC publication_status: published publisher: IEEE status: public title: (WIP) Towards the Automated Composition of Machine Learning Services type: conference user_id: '49109' year: '2018' ... --- _id: '19524' abstract: - lang: eng text: "Object ranking is an important problem in the realm of preference learning.\r\nOn the basis of training data in the form of a set of rankings of objects,\r\nwhich are typically represented as feature vectors, the goal is to learn a\r\nranking function that predicts a linear order of any new set of objects.\r\nCurrent approaches commonly focus on ranking by scoring, i.e., on learning an\r\nunderlying latent utility function that seeks to capture the inherent utility\r\nof each object. These approaches, however, are not able to take possible\r\neffects of context-dependence into account, where context-dependence means that\r\nthe utility or usefulness of an object may also depend on what other objects\r\nare available as alternatives. In this paper, we formalize the problem of\r\ncontext-dependent ranking and present two general approaches based on two\r\nnatural representations of context-dependent ranking functions. Both approaches\r\nare instantiated by means of appropriate neural network architectures, which\r\nare evaluated on suitable benchmark task." author: - first_name: Karlson full_name: Pfannschmidt, Karlson last_name: Pfannschmidt - first_name: Pritha full_name: Gupta, Pritha last_name: Gupta - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent Ranking Functions. arXiv:180305796. 2018. apa: Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. ArXiv:1803.05796. bibtex: '@article{Pfannschmidt_Gupta_Hüllermeier_2018, title={Deep Architectures for Learning Context-dependent Ranking Functions}, journal={arXiv:1803.05796}, author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2018} }' chicago: Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018. ieee: K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” arXiv:1803.05796. 2018. mla: Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018. short: K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018). date_created: 2020-09-17T10:53:39Z date_updated: 2022-01-06T06:54:06Z department: - _id: '7' - _id: '355' language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: arXiv:1803.05796 status: public title: Deep Architectures for Learning Context-dependent Ranking Functions type: preprint user_id: '13472' year: '2018' ... --- _id: '2857' 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 - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.' apa: 'Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.' bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }' chicago: Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever. “Programmatic Task Network Planning.” In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, 31–39. AAAI, 2018. ieee: F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, Delft, Netherlands, 2018, pp. 31–39. mla: Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39. short: 'F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.' conference: end_date: 2018-06-29 location: Delft, Netherlands name: 28th International Conference on Automated Planning and Scheduling start_date: 2018-06-24 date_created: 2018-05-24T09:00:20Z date_updated: 2022-01-06T06:58:08Z ddc: - '000' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:18:26Z date_updated: 2018-11-06T15:18:26Z file_id: '5384' file_name: Mohr18ProgrammaticPlanning.pdf file_size: 349958 relation: main_file success: 1 file_date_updated: 2018-11-06T15:18:26Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf oa: '1' page: 31-39 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning publisher: AAAI status: public title: Programmatic Task Network Planning type: conference user_id: '315' year: '2018' ... --- _id: '24150' author: - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Shalabh full_name: Bhatnagar, Shalabh last_name: Bhatnagar citation: ama: Ramaswamy A, Bhatnagar S. Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control. 2018;64(6):2614-2620. apa: Ramaswamy, A., & Bhatnagar, S. (2018). Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control, 64(6), 2614–2620. bibtex: '@article{Ramaswamy_Bhatnagar_2018, title={Stability of stochastic approximations with “controlled markov” noise and temporal difference learning}, volume={64}, number={6}, journal={IEEE Transactions on Automatic Control}, publisher={IEEE}, author={Ramaswamy, Arunselvan and Bhatnagar, Shalabh}, year={2018}, pages={2614–2620} }' chicago: 'Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” IEEE Transactions on Automatic Control 64, no. 6 (2018): 2614–20.' ieee: A. Ramaswamy and S. Bhatnagar, “Stability of stochastic approximations with ‘controlled markov’ noise and temporal difference learning,” IEEE Transactions on Automatic Control, vol. 64, no. 6, pp. 2614–2620, 2018. mla: Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” IEEE Transactions on Automatic Control, vol. 64, no. 6, IEEE, 2018, pp. 2614–20. short: A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620. date_created: 2021-09-10T10:17:54Z date_updated: 2022-01-06T06:56:08Z department: - _id: '355' intvolume: ' 64' issue: '6' language: - iso: eng page: 2614-2620 publication: IEEE Transactions on Automatic Control publisher: IEEE status: public title: Stability of stochastic approximations with “controlled markov” noise and temporal difference learning type: journal_article user_id: '66937' volume: 64 year: '2018' ... --- _id: '24151' author: - first_name: Burak full_name: Demirel, Burak last_name: Demirel - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Daniel E full_name: Quevedo, Daniel E last_name: Quevedo - first_name: Holger full_name: Karl, Holger last_name: Karl citation: ama: 'Demirel B, Ramaswamy A, Quevedo DE, Karl H. Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters. 2018;2(4):737-742.' apa: 'Demirel, B., Ramaswamy, A., Quevedo, D. E., & Karl, H. (2018). Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters, 2(4), 737–742.' bibtex: '@article{Demirel_Ramaswamy_Quevedo_Karl_2018, title={Deepcas: A deep reinforcement learning algorithm for control-aware scheduling}, volume={2}, number={4}, journal={IEEE Control Systems Letters}, publisher={IEEE}, author={Demirel, Burak and Ramaswamy, Arunselvan and Quevedo, Daniel E and Karl, Holger}, year={2018}, pages={737–742} }' chicago: 'Demirel, Burak, Arunselvan Ramaswamy, Daniel E Quevedo, and Holger Karl. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” IEEE Control Systems Letters 2, no. 4 (2018): 737–42.' ieee: 'B. Demirel, A. Ramaswamy, D. E. Quevedo, and H. Karl, “Deepcas: A deep reinforcement learning algorithm for control-aware scheduling,” IEEE Control Systems Letters, vol. 2, no. 4, pp. 737–742, 2018.' mla: 'Demirel, Burak, et al. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” IEEE Control Systems Letters, vol. 2, no. 4, IEEE, 2018, pp. 737–42.' short: B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters 2 (2018) 737–742. date_created: 2021-09-10T10:19:07Z date_updated: 2022-01-06T06:56:08Z department: - _id: '355' intvolume: ' 2' issue: '4' language: - iso: eng page: 737-742 publication: IEEE Control Systems Letters publisher: IEEE status: public title: 'Deepcas: A deep reinforcement learning algorithm for control-aware scheduling' type: journal_article user_id: '66937' volume: 2 year: '2018' ... --- _id: '2471' 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: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036' apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036' bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA}, title={On-The-Fly Service Construction with Prototypes}, DOI={10.1109/SCC.2018.00036}, booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service Construction with Prototypes.” In SCC. San Francisco, CA, USA: IEEE Computer Society, 2018. https://doi.org/10.1109/SCC.2018.00036.' ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018. mla: Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC, IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036. short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.' conference: end_date: 2018-07-07 location: San Francisco, CA, USA name: IEEE International Conference on Services Computing, SCC 2018 start_date: 2018-07-02 date_created: 2018-04-23T11:40:20Z date_updated: 2022-01-06T06:56:32Z ddc: - '000' department: - _id: '355' doi: 10.1109/SCC.2018.00036 file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:15:38Z date_updated: 2018-11-06T15:15:38Z file_id: '5383' file_name: 08456422.pdf file_size: 356132 relation: main_file success: 1 file_date_updated: 2018-11-06T15:15:38Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://ieeexplore.ieee.org/abstract/document/8456422 oa: '1' place: San Francisco, CA, USA project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: SCC publisher: IEEE Computer Society status: public title: On-The-Fly Service Construction with Prototypes type: conference user_id: '49109' year: '2018' ... --- _id: '3402' abstract: - lang: eng text: In machine learning, so-called nested dichotomies are utilized as a reduction technique, i.e., to decompose a multi-class classification problem into a set of binary problems, which are solved using a simple binary classifier as a base learner. The performance of the (multi-class) classifier thus produced strongly depends on the structure of the decomposition. In this paper, we conduct an empirical study, in which we compare existing heuristics for selecting a suitable structure in the form of a nested dichotomy. Moreover, we propose two additional heuristics as natural completions. One of them is the Best-of-K heuristic, which picks the (presumably) best among K randomly generated nested dichotomies. Surprisingly, and in spite of its simplicity, it turns out to outperform the state of the art. 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. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1' apa: 'Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1' bibtex: '@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}, DOI={10.1007/s10994-018-5733-1}, journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke}, year={2018} }' chicago: 'Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018. https://doi.org/10.1007/s10994-018-5733-1.' ieee: 'V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018.' mla: 'Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018, doi:10.1007/s10994-018-5733-1.' short: V. Melnikov, E. Hüllermeier, Machine Learning (2018). date_created: 2018-06-29T07:44:26Z date_updated: 2022-01-06T06:59:14Z ddc: - '000' department: - _id: '355' doi: 10.1007/s10994-018-5733-1 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T15:30:57Z date_updated: 2018-11-02T15:30:57Z file_id: '5305' file_name: OnTheEffectivenessOfHeuristics.pdf file_size: 1482882 relation: main_file success: 1 file_date_updated: 2018-11-02T15:30:57Z 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: Machine Learning publication_identifier: issn: - 1573-0565 status: public title: 'On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis' type: journal_article user_id: '15504' year: '2018' ... --- _id: '3510' abstract: - lang: eng text: Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches. article_type: original 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: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z' apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z' bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z.' ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi: 10.1007/s10994-018-5735-z.' mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.' short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515. conference: end_date: 2018-09-14 location: Dublin, Ireland name: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases start_date: 2018-09-10 date_created: 2018-07-08T14:06:14Z date_updated: 2022-01-06T06:59:21Z ddc: - '000' department: - _id: '355' - _id: '34' - _id: '7' - _id: '26' doi: 10.1007/s10994-018-5735-z file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T15:32:16Z date_updated: 2018-11-02T15:32:16Z file_id: '5306' file_name: ML-PlanAutomatedMachineLearnin.pdf file_size: 1070937 relation: main_file success: 1 file_date_updated: 2018-11-02T15:32:16Z has_accepted_license: '1' keyword: - AutoML - Hierarchical Planning - HTN planning - ML-Plan language: - iso: eng main_file_link: - open_access: '1' url: https://rdcu.be/3Nc2 oa: '1' page: 1495-1515 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: Machine Learning publication_identifier: eissn: - 1573-0565 issn: - 0885-6125 publication_status: epub_ahead publisher: Springer status: public title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning' type: journal_article user_id: '5786' year: '2018' ... --- _id: '3552' 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: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. doi:10.1007/978-3-030-01768-2_19' apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19 bibtex: '@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands}, title={Reduction Stumps for Multi-Class Classification}, DOI={10.1007/978-3-030-01768-2_19}, booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }' chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps for Multi-Class Classification.” In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands, n.d. https://doi.org/10.1007/978-3-030-01768-2_19. ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class Classification,” in Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands. mla: Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” Proceedings of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19. short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.' conference: end_date: 2018-10-26 location: ‘s-Hertogenbosch, the Netherlands name: Symposium on Intelligent Data Analysis start_date: 2018-10-24 date_created: 2018-07-13T15:29:15Z date_updated: 2022-01-06T06:59:25Z ddc: - '000' department: - _id: '355' doi: 10.1007/978-3-030-01768-2_19 file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:23:02Z date_updated: 2018-11-06T15:23:02Z file_id: '5385' file_name: Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf file_size: 1348768 relation: main_file success: 1 file_date_updated: 2018-11-06T15:23:02Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19 oa: '1' place: ‘s-Hertogenbosch, the Netherlands project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the Symposium on Intelligent Data Analysis publication_status: accepted quality_controlled: '1' status: public title: Reduction Stumps for Multi-Class Classification type: conference user_id: '49109' year: '2018' ... --- _id: '3852' abstract: - lang: eng text: "In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.\r\nVarious AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.\r\nMore specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.\r\nIn this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.\r\nWe evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT." 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. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.' apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden. bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }' chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018. ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018. mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” ICML 2018 AutoML Workshop, 2018. short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.' conference: end_date: 2018-07-15 location: Stockholm, Sweden name: ICML 2018 AutoML Workshop start_date: 2018-07-10 date_created: 2018-08-09T06:14:54Z date_updated: 2022-01-06T06:59:46Z ddc: - '006' department: - _id: '355' file: - access_level: open_access content_type: application/pdf creator: wever date_created: 2018-08-09T06:14:43Z date_updated: 2018-08-09T06:14:43Z file_id: '3853' file_name: 38.pdf file_size: 297811 relation: main_file file_date_updated: 2018-08-09T06:14:43Z has_accepted_license: '1' keyword: - automated machine learning - complex pipelines - hierarchical planning language: - iso: eng main_file_link: - url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx oa: '1' project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: ICML 2018 AutoML Workshop quality_controlled: '1' status: public title: ML-Plan for Unlimited-Length Machine Learning Pipelines type: conference urn: '38527' user_id: '49109' year: '2018' ... --- _id: '2109' abstract: - lang: eng text: In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance. 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. Ensembles of Evolved Nested Dichotomies for Classification. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM; 2018. doi:10.1145/3205455.3205562' apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM. https://doi.org/10.1145/3205455.3205562' bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles of Evolved Nested Dichotomies for Classification}, DOI={10.1145/3205455.3205562}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }' chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.” In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM, 2018. https://doi.org/10.1145/3205455.3205562.' ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018. mla: Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classification.” Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 2018, doi:10.1145/3205455.3205562. short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.' conference: end_date: 2018-07-19 location: Kyoto, Japan name: GECCO 2018 start_date: 2018-07-15 date_created: 2018-03-31T13:51:23Z date_updated: 2022-01-06T06:54:45Z ddc: - '000' department: - _id: '355' doi: 10.1145/3205455.3205562 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T14:33:54Z date_updated: 2018-11-02T14:33:54Z file_id: '5275' file_name: p561-wever.pdf file_size: 875404 relation: main_file success: 1 file_date_updated: 2018-11-02T14:33:54Z has_accepted_license: '1' keyword: - Classification - Hierarchical Decomposition - Indirect Encoding language: - iso: eng main_file_link: - open_access: '1' url: https://dl.acm.org/citation.cfm?doid=3205455.3205562 oa: '1' place: Kyoto, Japan 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, GECCO 2018, Kyoto, Japan, July 15-19, 2018 publication_status: published publisher: ACM status: public title: Ensembles of Evolved Nested Dichotomies for Classification type: conference user_id: '33176' year: '2018' ... --- _id: '17713' 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. Automated Multi-Label Classification based on ML-Plan. Published online 2018. apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv. bibtex: '@article{Wever_Mohr_Hüllermeier_2018, title={Automated Multi-Label Classification based on ML-Plan}, publisher={Arxiv}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }' chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018. ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018. mla: Wever, Marcel Dominik, et al. Automated Multi-Label Classification Based on ML-Plan. Arxiv, 2018. short: M.D. Wever, F. Mohr, E. Hüllermeier, (2018). date_created: 2020-08-07T11:38:10Z date_updated: 2022-01-06T06:53:17Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/1811.04060.pdf oa: '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 publisher: Arxiv status: public title: Automated Multi-Label Classification based on ML-Plan type: preprint user_id: '5786' year: '2018' ... --- _id: '17714' 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: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition. Published online 2018. apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine learning service composition. bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={Automated machine learning service composition}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }' chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine Learning Service Composition,” 2018. ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018. mla: Mohr, Felix, et al. Automated Machine Learning Service Composition. 2018. short: F. Mohr, M.D. Wever, E. Hüllermeier, (2018). date_created: 2020-08-07T11:40:13Z date_updated: 2022-01-06T06:53:17Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/1809.00486.pdf oa: '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 status: public title: Automated machine learning service composition type: preprint user_id: '5786' year: '2018' ... --- _id: '5693' author: - first_name: Helena full_name: Graf, Helena id: '52640' last_name: Graf citation: ama: Graf H. Ranking of Classification Algorithms in AutoML. Universität Paderborn; 2018. apa: Graf, H. (2018). Ranking of Classification Algorithms in AutoML. Universität Paderborn. bibtex: '@book{Graf_2018, title={Ranking of Classification Algorithms in AutoML}, publisher={Universität Paderborn}, author={Graf, Helena}, year={2018} }' chicago: Graf, Helena. Ranking of Classification Algorithms in AutoML. Universität Paderborn, 2018. ieee: H. Graf, Ranking of Classification Algorithms in AutoML. Universität Paderborn, 2018. mla: Graf, Helena. Ranking of Classification Algorithms in AutoML. Universität Paderborn, 2018. short: H. Graf, Ranking of Classification Algorithms in AutoML, Universität Paderborn, 2018. date_created: 2018-11-15T08:06:41Z date_updated: 2022-01-06T07:02:35Z department: - _id: '355' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publisher: Universität Paderborn status: public supervisor: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier title: Ranking of Classification Algorithms in AutoML type: bachelorsthesis user_id: '33176' year: '2018' ... --- _id: '5936' author: - first_name: Manuel full_name: Scheibl, Manuel last_name: Scheibl citation: ama: Scheibl M. Learning about Learning Curves from Dataset Properties. Universität Paderborn; 2018. apa: Scheibl, M. (2018). Learning about learning curves from dataset properties. Universität Paderborn. bibtex: '@book{Scheibl_2018, title={Learning about learning curves from dataset properties}, publisher={Universität Paderborn}, author={Scheibl, Manuel}, year={2018} }' chicago: Scheibl, Manuel. Learning about Learning Curves from Dataset Properties. Universität Paderborn, 2018. ieee: M. Scheibl, Learning about learning curves from dataset properties. Universität Paderborn, 2018. mla: Scheibl, Manuel. Learning about Learning Curves from Dataset Properties. Universität Paderborn, 2018. short: M. Scheibl, Learning about Learning Curves from Dataset Properties, Universität Paderborn, 2018. date_created: 2018-11-28T10:29:53Z date_updated: 2022-01-06T07:02:47Z department: - _id: '355' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publisher: Universität Paderborn status: public supervisor: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier title: Learning about learning curves from dataset properties type: bachelorsthesis user_id: '477' year: '2018' ... --- _id: '6423' 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: Discovery Science. Cham: Springer International Publishing; 2018:161-175. doi:10.1007/978-3-030-01771-2_11' apa: 'Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11' bibtex: '@inbook{Schäfer_Hüllermeier_2018, place={Cham}, title={Preference-Based Reinforcement Learning Using Dyad Ranking}, DOI={10.1007/978-3-030-01771-2_11}, booktitle={Discovery Science}, publisher={Springer International Publishing}, 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 Discovery Science, 161–75. Cham: Springer International Publishing, 2018. https://doi.org/10.1007/978-3-030-01771-2_11.' ieee: 'D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.' mla: Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” Discovery Science, Springer International Publishing, 2018, pp. 161–75, doi:10.1007/978-3-030-01771-2_11. short: 'D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International Publishing, Cham, 2018, pp. 161–175.' date_created: 2018-12-20T15:52:03Z date_updated: 2022-01-06T07:03:04Z ddc: - '000' department: - _id: '355' doi: 10.1007/978-3-030-01771-2_11 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2019-01-11T11:03:50Z date_updated: 2019-01-11T11:03:50Z file_id: '6623' file_name: Schäfer-Hüllermeier2018_Chapter_Preference-BasedReinforcementL.pdf file_size: 458972 relation: main_file success: 1 file_date_updated: 2019-01-11T11:03:50Z has_accepted_license: '1' language: - iso: eng page: 161-175 place: Cham project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: Discovery Science publication_identifier: isbn: - '9783030017705' - '9783030017712' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer International Publishing status: public title: Preference-Based Reinforcement Learning Using Dyad Ranking type: book_chapter user_id: '49109' year: '2018' ... --- _id: '10591' alternative_title: - Manifesto from Dagstuhl Perspectives Workshop 16151 citation: ama: Abiteboul S, Arenas M, Barceló P, et al., eds. Research Directions for Principles of Data Management. Vol 7.; 2018:1-29. apa: Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C., … Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29). bibtex: '@book{Abiteboul_Arenas_Barceló_Bienvenu_Calvanese_David_Hull_Hüllermeier_Kimelfeld_Libkin_et al._2018, title={Research Directions for Principles of Data Management}, volume={7}, number={1}, year={2018}, pages={1–29} }' chicago: Abiteboul, S., M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, et al., eds. Research Directions for Principles of Data Management. Vol. 7, 2018. ieee: S. Abiteboul et al., Eds., Research Directions for Principles of Data Management, vol. 7, no. 1. 2018, pp. 1–29. mla: Abiteboul, S., et al., editors. Research Directions for Principles of Data Management. Vol. 7, no. 1, 2018, pp. 1–29. short: S. Abiteboul, M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, E. Hüllermeier, B. Kimelfeld, L. Libkin, W. Martens, T. Milo, F. Murlak, F. Neven, M. Ortiz, T. Schwentick, J. Stoyanovich, J. Su, D. Suciu, V. Vianu, K. Yi, eds., Research Directions for Principles of Data Management, 2018. date_created: 2019-07-09T15:58:12Z date_updated: 2022-01-06T06:50:45Z department: - _id: '34' - _id: '7' - _id: '355' - _id: '26' editor: - first_name: S. full_name: Abiteboul, S. last_name: Abiteboul - first_name: M. full_name: Arenas, M. last_name: Arenas - first_name: P. full_name: Barceló, P. last_name: Barceló - first_name: M. full_name: Bienvenu, M. last_name: Bienvenu - first_name: D. full_name: Calvanese, D. last_name: Calvanese - first_name: C. full_name: David, C. last_name: David - first_name: R. full_name: Hull, R. last_name: Hull - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: B. full_name: Kimelfeld, B. last_name: Kimelfeld - first_name: L. full_name: Libkin, L. last_name: Libkin - first_name: W. full_name: Martens, W. last_name: Martens - first_name: T. full_name: Milo, T. last_name: Milo - first_name: F. full_name: Murlak, F. last_name: Murlak - first_name: F. full_name: Neven, F. last_name: Neven - first_name: M. full_name: Ortiz, M. last_name: Ortiz - first_name: T. full_name: Schwentick, T. last_name: Schwentick - first_name: J. full_name: Stoyanovich, J. last_name: Stoyanovich - first_name: J. full_name: Su, J. last_name: Su - first_name: D. full_name: Suciu, D. last_name: Suciu - first_name: V. full_name: Vianu, V. last_name: Vianu - first_name: K. full_name: Yi, K. last_name: Yi intvolume: ' 7' issue: '1' language: - iso: eng page: 1-29 status: public title: Research Directions for Principles of Data Management type: conference_editor user_id: '49109' volume: 7 year: '2018' ... --- _id: '10783' author: - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Couso I, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Mostaghim S, Nürnberger A, Borgelt C, eds. Frontiers in Computational Intelligence. Springer; 2018:31-46.' apa: 'Couso, I., & Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.' bibtex: '@inbook{Couso_Hüllermeier_2018, title={Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}, booktitle={Frontiers in Computational Intelligence}, publisher={Springer}, author={Couso, Ines and Hüllermeier, Eyke}, editor={Mostaghim, Sanaz and Nürnberger, Andreas and Borgelt, ChristianEditors}, year={2018}, pages={31–46} }' chicago: 'Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” In Frontiers in Computational Intelligence, edited by Sanaz Mostaghim, Andreas Nürnberger, and Christian Borgelt, 31–46. Springer, 2018.' ieee: 'I. Couso and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in Frontiers in Computational Intelligence, S. Mostaghim, A. Nürnberger, and C. Borgelt, Eds. Springer, 2018, pp. 31–46.' mla: 'Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” Frontiers in Computational Intelligence, edited by Sanaz Mostaghim et al., Springer, 2018, pp. 31–46.' short: 'I. Couso, E. Hüllermeier, in: S. Mostaghim, A. Nürnberger, C. Borgelt (Eds.), Frontiers in Computational Intelligence, Springer, 2018, pp. 31–46.' date_created: 2019-07-10T15:39:00Z date_updated: 2022-01-06T06:50:50Z department: - _id: '34' - _id: '7' - _id: '355' - _id: '26' editor: - first_name: Sanaz full_name: Mostaghim, Sanaz last_name: Mostaghim - first_name: Andreas full_name: Nürnberger, Andreas last_name: Nürnberger - first_name: Christian full_name: Borgelt, Christian last_name: Borgelt language: - iso: eng page: 31-46 publication: Frontiers in Computational Intelligence publisher: Springer status: public title: 'Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators' type: book_chapter user_id: '49109' year: '2018' ... --- _id: '16038' author: - first_name: D. full_name: Schäfer, D. 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, D. and Hüllermeier, Eyke}, year={2018}, pages={903–941} }' chicago: 'Schäfer, D., 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, D., 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: 2020-02-24T15:59:19Z date_updated: 2022-01-06T06:52:42Z 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: '10145' 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. 32 Nd AAAI Conference on Artificial Intelligence (AAAI). ; 2018:2951-2958.' apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958). bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_2018, title={Learning to Rank Based on Analogical Reasoning}, booktitle={Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI)}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2018}, pages={2951–2958} }' chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” In Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2951–58, 2018. ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank Based on Analogical Reasoning,” in Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958. mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–58. short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958.' date_created: 2019-06-07T08:49:33Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' - _id: '26' language: - iso: eng page: 2951-2958 publication: Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) status: public title: Learning to Rank Based on Analogical Reasoning type: conference user_id: '49109' year: '2018' ...