[{"doi":"10.1109/SCC.2018.00039","oa":"1","date_updated":"2022-01-06T06:56:35Z","language":[{"iso":"eng"}],"title":"(WIP) Towards the Automated Composition of Machine Learning Services","place":"San Francisco, CA, USA","publication_status":"published","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"department":[{"_id":"355"}],"_id":"2479","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"},"citation":{"short":"F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018.","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.","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","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","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.","mla":"Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.” SCC, IEEE, 2018, doi: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} }"},"year":"2018","type":"conference","main_file_link":[{"open_access":"1","url":"https://ieeexplore.ieee.org/document/8456425"}],"ddc":["000"],"user_id":"49109","status":"public","has_accepted_license":"1","date_created":"2018-04-24T08:34:52Z","publisher":"IEEE","author":[{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"first_name":"Amin","full_name":"Faez, Amin","last_name":"Faez"}],"file_date_updated":"2018-11-06T15:08:39Z","publication":"SCC","file":[{"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-06T15:08:39Z","creator":"wever","file_id":"5382","file_size":237890,"access_level":"closed","date_created":"2018-11-06T15:08:39Z","file_name":"08456425.pdf"}]},{"abstract":[{"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.","lang":"eng"}],"user_id":"13472","title":"Deep Architectures for Learning Context-dependent Ranking Functions","publication":"arXiv:1803.05796","department":[{"_id":"7"},{"_id":"355"}],"author":[{"full_name":"Pfannschmidt, Karlson","first_name":"Karlson","last_name":"Pfannschmidt"},{"last_name":"Gupta","full_name":"Gupta, Pritha","first_name":"Pritha"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2020-09-17T10:53:39Z","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"status":"public","date_updated":"2022-01-06T06:54:06Z","_id":"19524","language":[{"iso":"eng"}],"year":"2018","citation":{"short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).","ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” arXiv:1803.05796. 2018.","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.","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.","mla":"Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.","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} }"},"type":"preprint"},{"department":[{"_id":"355"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"title":"Programmatic Task Network Planning","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:58:08Z","oa":"1","file":[{"file_id":"5384","creator":"wever","file_size":349958,"relation":"main_file","success":1,"date_updated":"2018-11-06T15:18:26Z","content_type":"application/pdf","date_created":"2018-11-06T15:18:26Z","file_name":"Mohr18ProgrammaticPlanning.pdf","access_level":"closed"}],"file_date_updated":"2018-11-06T15:18:26Z","publication":"Proceedings of the 1st ICAPS Workshop on Hierarchical Planning","publisher":"AAAI","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"first_name":"Theodor","orcid":"0000-0001-5859-2457","full_name":"Lettmann, Theodor","last_name":"Lettmann","id":"315"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"date_created":"2018-05-24T09:00:20Z","has_accepted_license":"1","status":"public","user_id":"315","ddc":["000"],"main_file_link":[{"open_access":"1","url":"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf"}],"page":"31-39","type":"conference","citation":{"mla":"Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.","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} }","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.","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.","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."},"year":"2018","conference":{"end_date":"2018-06-29","start_date":"2018-06-24","name":"28th International Conference on Automated Planning and Scheduling","location":"Delft, Netherlands"},"_id":"2857"},{"publication":"IEEE Transactions on Automatic Control","department":[{"_id":"355"}],"publisher":"IEEE","author":[{"last_name":"Ramaswamy","id":"66937","first_name":"Arunselvan","full_name":"Ramaswamy, Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111"},{"last_name":"Bhatnagar","first_name":"Shalabh","full_name":"Bhatnagar, Shalabh"}],"volume":64,"date_created":"2021-09-10T10:17:54Z","status":"public","title":"Stability of stochastic approximations with “controlled markov” noise and temporal difference learning","user_id":"66937","page":"2614-2620","year":"2018","type":"journal_article","citation":{"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.","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.","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.","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.","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.","short":"A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620."},"language":[{"iso":"eng"}],"intvolume":" 64","_id":"24150","date_updated":"2022-01-06T06:56:08Z","issue":"6"},{"volume":2,"status":"public","date_created":"2021-09-10T10:19:07Z","author":[{"last_name":"Demirel","full_name":"Demirel, Burak","first_name":"Burak"},{"last_name":"Ramaswamy","id":"66937","first_name":"Arunselvan","full_name":"Ramaswamy, Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111"},{"full_name":"Quevedo, Daniel E","first_name":"Daniel E","last_name":"Quevedo"},{"last_name":"Karl","first_name":"Holger","full_name":"Karl, Holger"}],"publisher":"IEEE","publication":"IEEE Control Systems Letters","department":[{"_id":"355"}],"title":"Deepcas: A deep reinforcement learning algorithm for control-aware scheduling","user_id":"66937","type":"journal_article","year":"2018","citation":{"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.","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} }","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.","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."},"page":"737-742","language":[{"iso":"eng"}],"issue":"4","intvolume":" 2","_id":"24151","date_updated":"2022-01-06T06:56:08Z"},{"ddc":["000"],"user_id":"49109","status":"public","has_accepted_license":"1","date_created":"2018-04-23T11:40:20Z","author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publisher":"IEEE Computer Society","publication":"SCC","file_date_updated":"2018-11-06T15:15:38Z","file":[{"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-06T15:15:38Z","creator":"wever","file_id":"5383","file_size":356132,"access_level":"closed","date_created":"2018-11-06T15:15:38Z","file_name":"08456422.pdf"}],"_id":"2471","conference":{"end_date":"2018-07-07","name":"IEEE International Conference on Services Computing, SCC 2018","start_date":"2018-07-02","location":"San Francisco, CA, USA"},"year":"2018","citation":{"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} }","mla":"Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC, IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036.","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.","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","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018."},"type":"conference","main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/8456422","open_access":"1"}],"title":"On-The-Fly Service Construction with Prototypes","place":"San Francisco, CA, USA","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"355"}],"doi":"10.1109/SCC.2018.00036","oa":"1","date_updated":"2022-01-06T06:56:32Z","language":[{"iso":"eng"}]},{"doi":"10.1007/s10994-018-5733-1","date_updated":"2022-01-06T06:59:14Z","language":[{"iso":"eng"}],"title":"On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis","publication_identifier":{"issn":["1573-0565"]},"project":[{"_id":"11","name":"SFB 901 - Subproject B3"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"1","name":"SFB 901"}],"department":[{"_id":"355"}],"_id":"3402","citation":{"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.","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","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.","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} }","short":"V. Melnikov, E. Hüllermeier, Machine Learning (2018).","ieee":"V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018."},"year":"2018","type":"journal_article","ddc":["000"],"user_id":"15504","abstract":[{"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.","lang":"eng"}],"has_accepted_license":"1","status":"public","date_created":"2018-06-29T07:44:26Z","author":[{"last_name":"Melnikov","first_name":"Vitalik","full_name":"Melnikov, Vitalik"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication":"Machine Learning","file_date_updated":"2018-11-02T15:30:57Z","file":[{"file_id":"5305","creator":"ups","file_size":1482882,"relation":"main_file","success":1,"content_type":"application/pdf","date_updated":"2018-11-02T15:30:57Z","file_name":"OnTheEffectivenessOfHeuristics.pdf","date_created":"2018-11-02T15:30:57Z","access_level":"closed"}]},{"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publication_identifier":{"issn":["0885-6125"],"eissn":["1573-0565"]},"publication_status":"epub_ahead","department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","language":[{"iso":"eng"}],"oa":"1","doi":"10.1007/s10994-018-5735-z","date_updated":"2022-01-06T06:59:21Z","has_accepted_license":"1","status":"public","date_created":"2018-07-08T14:06:14Z","file":[{"access_level":"closed","date_created":"2018-11-02T15:32:16Z","file_name":"ML-PlanAutomatedMachineLearnin.pdf","success":1,"relation":"main_file","date_updated":"2018-11-02T15:32:16Z","content_type":"application/pdf","file_id":"5306","creator":"ups","file_size":1070937}],"author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publisher":"Springer","publication":"Machine Learning","keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"file_date_updated":"2018-11-02T15:32:16Z","user_id":"5786","ddc":["000"],"article_type":"original","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."}],"type":"journal_article","year":"2018","citation":{"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.","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","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.","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} }","short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","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."},"page":"1495-1515","main_file_link":[{"url":"https://rdcu.be/3Nc2","open_access":"1"}],"_id":"3510","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"}},{"publication_status":"accepted","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"SFB 901 - Project Area B","_id":"3"}],"department":[{"_id":"355"}],"title":"Reduction Stumps for Multi-Class Classification","place":"‘s-Hertogenbosch, the Netherlands","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-01768-2_19","oa":"1","date_updated":"2022-01-06T06:59:25Z","status":"public","has_accepted_license":"1","date_created":"2018-07-13T15:29:15Z","quality_controlled":"1","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"file_date_updated":"2018-11-06T15:23:02Z","publication":"Proceedings of the Symposium on Intelligent Data Analysis","file":[{"relation":"main_file","success":1,"date_updated":"2018-11-06T15:23:02Z","content_type":"application/pdf","file_id":"5385","creator":"wever","file_size":1348768,"access_level":"closed","date_created":"2018-11-06T15:23:02Z","file_name":"Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf"}],"ddc":["000"],"user_id":"49109","citation":{"short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.","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.","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","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.","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} }","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."},"year":"2018","type":"conference","main_file_link":[{"open_access":"1","url":"https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19"}],"_id":"3552","conference":{"name":"Symposium on Intelligent Data Analysis","start_date":"2018-10-24","location":"‘s-Hertogenbosch, the Netherlands","end_date":"2018-10-26"}},{"citation":{"short":"M.D. Wever, F. Mohr, E. Hüllermeier, 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.","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.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.","mla":"Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” ICML 2018 AutoML Workshop, 2018.","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} }"},"type":"conference","year":"2018","main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"urn":"38527","conference":{"name":"ICML 2018 AutoML Workshop","start_date":"2018-07-10","location":"Stockholm, Sweden","end_date":"2018-07-15"},"_id":"3852","date_created":"2018-08-09T06:14:54Z","status":"public","has_accepted_license":"1","file":[{"file_name":"38.pdf","date_created":"2018-08-09T06:14:43Z","access_level":"open_access","file_size":297811,"creator":"wever","file_id":"3853","date_updated":"2018-08-09T06:14:43Z","content_type":"application/pdf","relation":"main_file"}],"keyword":["automated machine learning","complex pipelines","hierarchical planning"],"publication":"ICML 2018 AutoML Workshop","file_date_updated":"2018-08-09T06:14:43Z","quality_controlled":"1","author":[{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"user_id":"49109","ddc":["006"],"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."}],"language":[{"iso":"eng"}],"oa":"1","date_updated":"2022-01-06T06:59:46Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"355"}],"title":"ML-Plan for Unlimited-Length Machine Learning Pipelines"}]