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448 Publications


2021 | Conference Paper | LibreCat-ID: 24382
Gevers, K., Schöppner, V., & Hüllermeier, E. (2021). Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. International Institute of Welding, online.
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2021 | Journal Article | LibreCat-ID: 21004
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276
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2021 | Journal Article | LibreCat-ID: 21092
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
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2021 | Journal Article | LibreCat-ID: 21535
Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., & Hüllermeier, E. (2021). Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research, 22(7), 1–108.
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2021 | Conference Paper | LibreCat-ID: 21570
Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.
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2021 | Conference Paper | LibreCat-ID: 23779
Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.
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2021 | Conference Paper | LibreCat-ID: 22913
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).
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2021 | Conference Paper | LibreCat-ID: 22914
Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.
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2021 | Preprint | LibreCat-ID: 21600 | OA
Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2021). Efficient time stepping for numerical integration using reinforcement  learning. In arXiv:2104.03562.
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2021 | Conference Paper | LibreCat-ID: 27381
Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer. https://doi.org/10.1007/978-3-030-88942-5
LibreCat | DOI | arXiv
 

2021 | Dissertation | LibreCat-ID: 27284 | OA
Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification. https://doi.org/10.17619/UNIPB/1-1302
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2021 | Conference Paper | LibreCat-ID: 21198
Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.
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2020 | Book Chapter | LibreCat-ID: 19521
Pfannschmidt, K., & Hüllermeier, E. (2020). Learning Choice Functions via Pareto-Embeddings. In Lecture Notes in Computer Science. Cham. https://doi.org/10.1007/978-3-030-58285-2_30
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2020 | Conference Paper | LibreCat-ID: 19953 | OA
Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.
LibreCat | Files available | arXiv
 

2020 | Conference Paper | LibreCat-ID: 21534
Bengs, V., & Hüllermeier, E. (2020). Preselection Bandits. In International Conference on Machine Learning (pp. 778–787).
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2020 | Preprint | LibreCat-ID: 21536
Bengs, V., & Hüllermeier, E. (2020). Multi-Armed Bandits with Censored Consumption of Resources. ArXiv:2011.00813.
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2020 | Conference Paper | LibreCat-ID: 17407
Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.
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2020 | Conference Paper | LibreCat-ID: 17408
Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.
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2020 | Conference Paper | LibreCat-ID: 17424
Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8
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2020 | Preprint | LibreCat-ID: 17605 | OA
Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.
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2020 | Conference Paper | LibreCat-ID: 20306
Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.
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2020 | Book Chapter | LibreCat-ID: 18014
El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22
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2020 | Preprint | LibreCat-ID: 18017
El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. ArXiv:2002.04275.
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2020 | Conference Paper | LibreCat-ID: 18276
Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.
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2020 | Journal Article | LibreCat-ID: 16725
Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
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2020 | Conference Paper | LibreCat-ID: 15629
Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.
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2020 | Journal Article | LibreCat-ID: 15025
Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266
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2019 | Preprint | LibreCat-ID: 19523
Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. ArXiv:1901.10860.
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2019 | Journal Article | LibreCat-ID: 17565
Merten, M.-L., Seemann, N., & Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch, 142, 124–146.
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2019 | Preprint | LibreCat-ID: 18018
Bengs, V., & Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. ArXiv:1903.09864.
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2019 | Conference Abstract | LibreCat-ID: 8868
Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.
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2019 | Journal Article | LibreCat-ID: 10578
Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 191–213.
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2019 | Journal Article | LibreCat-ID: 15001
Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642
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2019 | Journal Article | LibreCat-ID: 15002 | OA
Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5
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2019 | Conference Paper | LibreCat-ID: 15003
Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.
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2019 | Book Chapter | LibreCat-ID: 15004
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22
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2019 | Book Chapter | LibreCat-ID: 15005
Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In KI 2019: Advances in Artificial Intelligence. Cham. https://doi.org/10.1007/978-3-030-30179-8_3
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2019 | Book Chapter | LibreCat-ID: 15006
Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7
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2019 | Conference Paper | LibreCat-ID: 15007 | OA
Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). https://doi.org/10.1016/j.jmva.2019.02.017
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2019 | Conference Paper | LibreCat-ID: 15009
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
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2019 | Conference Paper | LibreCat-ID: 15011 | OA
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.
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2019 | Conference Paper | LibreCat-ID: 15013
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.
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2019 | Conference Paper | LibreCat-ID: 15014
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.
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2019 | Journal Article | LibreCat-ID: 15015
Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572
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2019 | Journal Article | LibreCat-ID: 14027
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
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2019 | Journal Article | LibreCat-ID: 14028
Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation. Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555
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2019 | Conference Abstract | LibreCat-ID: 13132
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.
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2019 | Conference Paper | LibreCat-ID: 10232 | OA
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.
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2018 | Conference Paper | LibreCat-ID: 2479 | OA
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
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2018 | Preprint | LibreCat-ID: 19524
Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. ArXiv:1803.05796.
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2018 | Conference Paper | LibreCat-ID: 2857 | OA
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.
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2018 | Journal Article | LibreCat-ID: 24150
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.
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2018 | Journal Article | LibreCat-ID: 24151
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.
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2018 | Conference Paper | LibreCat-ID: 2471 | OA
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
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2018 | Journal Article | LibreCat-ID: 3402
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
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2018 | Journal Article | LibreCat-ID: 3510 | OA
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
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2018 | Conference Paper | LibreCat-ID: 3552 | OA
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
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2018 | Conference Paper | LibreCat-ID: 3852 | OA
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
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2018 | Conference Paper | LibreCat-ID: 2109 | OA
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
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2018 | Preprint | LibreCat-ID: 17713 | OA
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv.
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2018 | Preprint | LibreCat-ID: 17714 | OA
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine learning service composition.
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2018 | Bachelorsthesis | LibreCat-ID: 5693
Graf, H. (2018). Ranking of Classification Algorithms in AutoML. Universität Paderborn.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Scheibl, M. (2018). Learning about learning curves from dataset properties. Universität Paderborn.
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2018 | Book Chapter | LibreCat-ID: 6423
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
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2018 | Conference (Editor) | LibreCat-ID: 10591
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).
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2018 | Book Chapter | LibreCat-ID: 10783
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.
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2018 | Journal Article | LibreCat-ID: 16038
Schäfer, D., & Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Conference Paper | LibreCat-ID: 10145
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).
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2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul, A., Hüllermeier, E., & Busa-Fekete, R. (2018). Ranking Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine Learning (ICML), 3469–3477.
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2018 | Conference Paper | LibreCat-ID: 10149
Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 15–20.
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2018 | Book Chapter | LibreCat-ID: 10152
Mencia, E. L., Fürnkranz, J., Hüllermeier, E., & Rapp, M. (2018). Learning interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, & M. A. J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 81–113). Springer.
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2018 | Conference Paper | LibreCat-ID: 10181
Nguyen, V.-L., Destercke, S., Masson, M.-H., & Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–5095.
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2018 | Conference Paper | LibreCat-ID: 10184
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. Proc. 21st Int. Conference on Discovery Science (DS), 161–175.
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2018 | Journal Article | LibreCat-ID: 10276
Schäfer, D., & Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Conference Abstract | LibreCat-ID: 1379 | OA
Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., & Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart & D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. Stuttgart, Germany.
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2017 | Journal Article | LibreCat-ID: 24152
Ramaswamy, A., & Bhatnagar, S. (2017). Analysis of gradient descent methods with nondiminishing bounded errors. IEEE Transactions on Automatic Control, 63(5), 1465–1471.
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2017 | Journal Article | LibreCat-ID: 24153
Ramaswamy, A., & Bhatnagar, S. (2017). A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions. Mathematics of Operations Research, 42(3), 648–661.
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2017 | Conference Paper | LibreCat-ID: 3325
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000074341
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2017 | Conference Paper | LibreCat-ID: 115
Jakobs, M.-C., Krämer, J., van Straaten, D., & Lettmann, T. (2017). Certification Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 7–12).
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2017 | Conference Paper | LibreCat-ID: 1158
Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., & Hüllermeier, E. (2017). Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 40–45). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W17-2206
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2017 | Book Chapter | LibreCat-ID: 18167
Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., & Hlüllermeier, E. (2017). Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In Proceedings of Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature.
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2017 | Bachelorsthesis | LibreCat-ID: 5694
Schnitker, N. N. (2017). Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn.
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2017 | Conference Abstract | LibreCat-ID: 5722
Gupta, P., Hetzer, A., Tornede, T., Gottschalk, S., Kornelsen, A., Osterbrink, S., … Hüllermeier, E. (2017). jPL: A Java-based Software Framework for Preference Learning. Presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock.
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2017 | Mastersthesis | LibreCat-ID: 5724
Hetzer, A., & Tornede, T. (2017). Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn.
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2017 | Conference Paper | LibreCat-ID: 71
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262
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2017 | Report | LibreCat-ID: 72
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.
LibreCat | Files available
 

2017 | Encyclopedia Article | LibreCat-ID: 10589
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).
LibreCat
 

2017 | Book Chapter | LibreCat-ID: 10784
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer.
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 1180 | OA
Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.
LibreCat | Files available | Download (ext.)
 

2017 | Conference Paper | LibreCat-ID: 15397
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the structure of nested dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany (pp. 1–12). KIT Scientific Publishing.
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 15399
Czech, M., Hüllermeier, E., Jacobs, M. C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany.
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 15110
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data. In in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer.
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 10204
Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T., … Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 10206 | OA
Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206). https://doi.org/10.1007/978-3-319-67190-1_15
LibreCat | Files available | DOI
 

2017 | Conference Paper | LibreCat-ID: 10207
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26).
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 10208
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16).
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 10212
Hoffmann, F., Hüllermeier, E., & Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017.
LibreCat
 

2017 | Conference Paper | LibreCat-ID: 10213
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 1–12).
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