--- _id: '15009' author: - first_name: Nico full_name: Epple, Nico last_name: Epple - first_name: Simone full_name: Dari, Simone last_name: Dari - first_name: Ludwig full_name: Drees, Ludwig last_name: Drees - first_name: Valentin full_name: Protschky, Valentin last_name: Protschky - first_name: Andreas full_name: Riener, Andreas last_name: Riener citation: ama: 'Epple N, Dari S, Drees L, Protschky V, Riener A. Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In: 2019 IEEE Intelligent Vehicles Symposium (IV). ; 2019. doi:10.1109/ivs.2019.8814100' apa: Epple, N., Dari, S., Drees, L., Protschky, V., & Riener, A. (2019). Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In 2019 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2019.8814100 bibtex: '@inproceedings{Epple_Dari_Drees_Protschky_Riener_2019, title={Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries}, DOI={10.1109/ivs.2019.8814100}, booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, author={Epple, Nico and Dari, Simone and Drees, Ludwig and Protschky, Valentin and Riener, Andreas}, year={2019} }' chicago: Epple, Nico, Simone Dari, Ludwig Drees, Valentin Protschky, and Andreas Riener. “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries.” In 2019 IEEE Intelligent Vehicles Symposium (IV), 2019. https://doi.org/10.1109/ivs.2019.8814100. ieee: N. Epple, S. Dari, L. Drees, V. Protschky, and A. Riener, “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries,” in 2019 IEEE Intelligent Vehicles Symposium (IV), 2019. mla: Epple, Nico, et al. “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries.” 2019 IEEE Intelligent Vehicles Symposium (IV), 2019, doi:10.1109/ivs.2019.8814100. short: 'N. Epple, S. Dari, L. Drees, V. Protschky, A. Riener, in: 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.' date_created: 2019-11-15T10:54:04Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' doi: 10.1109/ivs.2019.8814100 language: - iso: eng publication: 2019 IEEE Intelligent Vehicles Symposium (IV) publication_identifier: isbn: - '9781728105604' publication_status: published status: public title: Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries type: conference user_id: '315' year: '2019' ... --- _id: '15011' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.' apa: 'Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.' bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2019, title={Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking}, booktitle={Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019}, publisher={KIT Scientific Publishing, Karlsruhe}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Hoffmann, Frank and Hüllermeier, Eyke and Mikut, RalfEditors}, year={2019}, pages={135–146} }' chicago: 'Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, 135–46. KIT Scientific Publishing, Karlsruhe, 2019.' ieee: 'A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019, pp. 135–146.' mla: 'Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–46.' short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.' conference: end_date: 2019-11-29 location: Dortmund name: 29. Workshop Computational Intelligence start_date: 2019-11-28 date_created: 2019-11-15T13:29:25Z date_updated: 2022-01-06T06:52:14Z ddc: - '006' department: - _id: '355' editor: - first_name: Frank full_name: Hoffmann, Frank last_name: Hoffmann - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier - first_name: Ralf full_name: Mikut, Ralf last_name: Mikut file: - access_level: open_access content_type: application/pdf creator: ahetzer date_created: 2020-05-25T08:01:31Z date_updated: 2020-05-25T08:01:31Z file_id: '17060' file_name: ci_workshop_tornede.pdf file_size: 468825 relation: main_file file_date_updated: 2020-05-25T08:01:31Z has_accepted_license: '1' language: - iso: eng oa: '1' page: 135-146 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 - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 publication_identifier: isbn: - 978-3-7315-0979-0 publication_status: published publisher: KIT Scientific Publishing, Karlsruhe status: public title: 'Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking' type: conference user_id: '38209' year: '2019' ... --- _id: '15013' author: - first_name: Klaus full_name: Brinker, Klaus last_name: Brinker - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Brinker K, Hüllermeier E. A Reduction of Label Ranking to Multiclass Classification. In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany; 2019.' apa: Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany. bibtex: '@inproceedings{Brinker_Hüllermeier_2019, place={Würzburg, Germany}, title={A Reduction of Label Ranking to Multiclass Classification}, booktitle={Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases}, author={Brinker, Klaus and Hüllermeier, Eyke}, year={2019} }' chicago: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass Classification.” In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany, 2019. ieee: K. Brinker and E. Hüllermeier, “A Reduction of Label Ranking to Multiclass Classification,” in Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2019. mla: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass Classification.” Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2019. short: 'K. Brinker, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Würzburg, Germany, 2019.' date_created: 2019-11-18T07:26:43Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' - _id: '7' language: - iso: eng place: Würzburg, Germany publication: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases status: public title: A Reduction of Label Ranking to Multiclass Classification type: conference user_id: '315' year: '2019' ... --- _id: '15014' author: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: Sebastian full_name: Diestercke, Sebastian last_name: Diestercke citation: ama: 'Hüllermeier E, Couso I, Diestercke S. Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management. ; 2019.' apa: 'Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.' bibtex: '@inproceedings{Hüllermeier_Couso_Diestercke_2019, title={Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants}, booktitle={Proceedings SUM 2019, International Conference on Scalable Uncertainty Management}, author={Hüllermeier, Eyke and Couso, Ines and Diestercke, Sebastian}, year={2019} }' chicago: 'Hüllermeier, Eyke, Ines Couso, and Sebastian Diestercke. “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' ieee: 'E. Hüllermeier, I. Couso, and S. Diestercke, “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants,” in Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' mla: 'Hüllermeier, Eyke, et al. “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' short: 'E. Hüllermeier, I. Couso, S. Diestercke, in: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.' date_created: 2019-11-18T07:38:13Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' - _id: '7' language: - iso: eng publication: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management status: public title: 'Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants' type: conference user_id: '315' year: '2019' ... --- _id: '15015' author: - first_name: Sascha full_name: Henzgen, Sascha last_name: Henzgen - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge Discovery from Data. 2019:1-36. doi:10.1145/3363572 apa: Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572 bibtex: '@article{Henzgen_Hüllermeier_2019, title={Mining Rank Data}, DOI={10.1145/3363572}, journal={ACM Transactions on Knowledge Discovery from Data}, author={Henzgen, Sascha and Hüllermeier, Eyke}, year={2019}, pages={1–36} }' chicago: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions on Knowledge Discovery from Data, 2019, 1–36. https://doi.org/10.1145/3363572. ieee: S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on Knowledge Discovery from Data, pp. 1–36, 2019. mla: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions on Knowledge Discovery from Data, 2019, pp. 1–36, doi:10.1145/3363572. short: S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from Data (2019) 1–36. date_created: 2019-11-18T07:40:27Z date_updated: 2022-01-06T06:52:14Z department: - _id: '34' - _id: '355' - _id: '7' doi: 10.1145/3363572 language: - iso: eng page: 1-36 publication: ACM Transactions on Knowledge Discovery from Data publication_identifier: issn: - 1556-4681 publication_status: published status: public title: Mining Rank Data type: journal_article user_id: '315' year: '2019' ... --- _id: '14027' author: - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Matthias full_name: Eulert, Matthias last_name: Eulert - first_name: Hajo full_name: Holzmann, Hajo last_name: Holzmann citation: ama: Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis. 2019:291-312. doi:10.1016/j.jmva.2019.02.017 apa: Bengs, V., Eulert, M., & Holzmann, H. (2019). Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017 bibtex: '@article{Bengs_Eulert_Holzmann_2019, title={Asymptotic confidence sets for the jump curve in bivariate regression problems}, DOI={10.1016/j.jmva.2019.02.017}, journal={Journal of Multivariate Analysis}, author={Bengs, Viktor and Eulert, Matthias and Holzmann, Hajo}, year={2019}, pages={291–312} }' chicago: Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate Analysis, 2019, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017. ieee: V. Bengs, M. Eulert, and H. Holzmann, “Asymptotic confidence sets for the jump curve in bivariate regression problems,” Journal of Multivariate Analysis, pp. 291–312, 2019. mla: Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate Analysis, 2019, pp. 291–312, doi:10.1016/j.jmva.2019.02.017. short: V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312. date_created: 2019-10-30T14:22:57Z date_updated: 2022-01-06T06:51:52Z department: - _id: '34' - _id: '355' doi: 10.1016/j.jmva.2019.02.017 language: - iso: eng page: 291-312 publication: Journal of Multivariate Analysis publication_identifier: issn: - 0047-259X publication_status: published status: public title: Asymptotic confidence sets for the jump curve in bivariate regression problems type: journal_article user_id: '76599' year: '2019' ... --- _id: '14028' author: - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Hajo full_name: Holzmann, Hajo last_name: Holzmann citation: ama: Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555 apa: Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation. Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555 bibtex: '@article{Bengs_Holzmann_2019, title={Adaptive confidence sets for kink estimation}, DOI={10.1214/19-ejs1555}, journal={Electronic Journal of Statistics}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019}, pages={1523–1579} }' chicago: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, 1523–79. https://doi.org/10.1214/19-ejs1555. ieee: V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” Electronic Journal of Statistics, pp. 1523–1579, 2019. mla: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555. short: V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579. date_created: 2019-10-30T14:25:16Z date_updated: 2022-01-06T06:51:52Z department: - _id: '34' - _id: '355' doi: 10.1214/19-ejs1555 language: - iso: eng page: 1523-1579 publication: Electronic Journal of Statistics publication_identifier: issn: - 1935-7524 publication_status: published status: public title: Adaptive confidence sets for kink estimation type: journal_article user_id: '76599' year: '2019' ... --- _id: '13132' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.' apa: 'Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.' bibtex: '@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.' ieee: 'F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.' mla: 'Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., 2019, pp. 273–74.' short: 'F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274.' conference: end_date: 2019-09-26 location: Kassel name: Informatik 2019 start_date: 2019-09-23 date_created: 2019-09-04T08:44:46Z date_updated: 2022-01-06T06:51:28Z department: - _id: '355' language: - iso: eng page: ' 273-274 ' place: Bonn project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: 'INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft' publisher: Gesellschaft für Informatik e.V. series_title: INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik status: public title: From Automated to On-The-Fly Machine Learning type: conference_abstract user_id: '38209' year: '2019' ... --- _id: '10232' abstract: - lang: eng text: Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with. 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: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.' apa: Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA. bibtex: '@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }' chicago: Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019. ieee: M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019. mla: Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019. short: 'M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.' conference: end_date: 2019-06-15 location: Long Beach, CA, USA name: 6th ICML Workshop on Automated Machine Learning (AutoML 2019) start_date: 2019-06-09 date_created: 2019-06-11T21:33:06Z date_updated: 2022-01-06T06:50:33Z ddc: - '006' department: - _id: '355' file: - access_level: open_access content_type: application/pdf creator: wever date_created: 2019-09-10T08:19:01Z date_updated: 2019-09-10T08:20:44Z file_id: '13177' file_name: Automating_MultiLabel_Classification_Extending_ML-Plan.pdf file_size: 388191 relation: main_file file_date_updated: 2019-09-10T08:20:44Z has_accepted_license: '1' language: - iso: eng 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: Automating Multi-Label Classification Extending ML-Plan type: conference user_id: '33176' year: '2019' ... --- _id: '20243' author: - first_name: Katharina full_name: Rohlfing, Katharina id: '50352' last_name: Rohlfing - first_name: Giuseppe full_name: Leonardi, Giuseppe last_name: Leonardi - first_name: Iris full_name: Nomikou, Iris last_name: Nomikou - first_name: Joanna full_name: Rączaszek-Leonardi, Joanna last_name: Rączaszek-Leonardi - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991' apa: 'Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991' bibtex: '@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019, title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}, DOI={10.1109/TCDS.2019.2892991}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna and Hüllermeier, Eyke}, year={2019} }' chicago: 'Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019. https://doi.org/10.1109/TCDS.2019.2892991.' ieee: 'K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991.' mla: 'Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991.' short: K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019). date_created: 2020-11-02T13:25:49Z date_updated: 2023-02-01T12:39:19Z department: - _id: '749' - _id: '355' doi: 10.1109/TCDS.2019.2892991 language: - iso: eng publication: IEEE Transactions on Cognitive and Developmental Systems status: public title: 'Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches' type: journal_article user_id: '14931' year: '2019' ... --- _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' ...