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184 Publications


2019 | Journal Article | LibreCat-ID: 15002
W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction: a unifying view on problems and methods,” Data Mining and Knowledge Discovery, vol. 33, no. 2, pp. 293–324, 2019.
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2019 | Journal Article | LibreCat-ID: 14028
V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” Electronic Journal of Statistics, pp. 1523–1579, 2019.
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2019 | Conference Paper | LibreCat-ID: 15007
V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.
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2019 | Conference Paper | LibreCat-ID: 15014
E. Hüllermeier, I. Couso, and S. Diestercke, “Learning from Imprecise Data: {A}djustments of Optimistic and Pessimistic Variants,” in Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.
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2019 | Conference Paper | LibreCat-ID: 15003
T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “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, 2019.
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2019 | Journal Article | LibreCat-ID: 15015
S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on Knowledge Discovery from Data, pp. 1–36, 2019.
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2019 | Conference Paper | LibreCat-ID: 10232
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.
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2019 | Conference Paper | LibreCat-ID: 15009
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.
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2019 | Book Chapter | LibreCat-ID: 15004
M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based Learning to Rank,” in Discovery Science, Cham, 2019.
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2019 | Conference Paper | LibreCat-ID: 15011
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.
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2019 | Journal Article | LibreCat-ID: 10578
V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated by Mallows and Plackett–Luce Relations,” New Mathematics and Natural Computation, vol. 15, no. 2, pp. 191–213, 2019.
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2019 | Conference Abstract | LibreCat-ID: 8956
A. Hetzer, M. D. Wever, F. Mohr, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” presented at the European Conference on Data Analysis (ECDA), Bayreuth, Germany, 2019.
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2019 | Book Chapter | LibreCat-ID: 15005
M. Ahmadi Fahandar and E. Hüllermeier, “Analogy-Based Preference Learning with Kernels,” in KI 2019: Advances in Artificial Intelligence, Cham, 2019.
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2019 | Conference Abstract | LibreCat-ID: 8868
M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine Learning for Multi-Label Classification,” presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany, 2019.
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2019 | Conference Abstract | LibreCat-ID: 13132
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.
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2019 | Journal Article | LibreCat-ID: 15001
I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning,” IEEE Computational Intelligence Magazine, pp. 31–44, 2019.
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2019 | Journal Article | LibreCat-ID: 14027
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.
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2019 | Book Chapter | LibreCat-ID: 15006
V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,” in Discovery Science, Cham, 2019.
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2019 | Conference Paper | LibreCat-ID: 15013
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.
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2018 | Conference Paper | LibreCat-ID: 10184
D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175.
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2018 | Conference Paper | LibreCat-ID: 3852
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
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2018 | Conference Paper | LibreCat-ID: 2479
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.
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2018 | Conference Paper | LibreCat-ID: 10153
F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-class Classification,” in Proc. 17th Int. Symposium on Intelligent Data Analysis (IDA), 2018, pp. 225–237.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
M. Scheibl, Learning about learning curves from dataset properties. 2018.
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2018 | Conference Paper | LibreCat-ID: 10185
N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M. D. Wever, and E. Hüllermeier, “Supporting the Cognitive Process in Annotation Tasks,” in Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, 2018.
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2018 | Conference Paper | LibreCat-ID: 10154
F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in Proc. 15th Int. Conference on Services Computing (SCC), 2018, pp. 241–244.
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2018 | Conference Paper | LibreCat-ID: 10192
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in Int. Workshop on Automatic Machine Learning (AutoML) at ICML 2018, 2018.
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2018 | Journal Article | LibreCat-ID: 10274
V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirial analysis,” Machine Learning, vol. 107, no. 8–10, pp. 1537–1560, 2018.
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2018 | Conference (Editor) | LibreCat-ID: 10591
S. Abiteboul et al., Eds., Research Directions for Principles of Data Management, vol. 7, no. 1. 2018, pp. 1–29.
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2018 | Conference Paper | LibreCat-ID: 10181
V.-L. Nguyen, S. Destercke, M.-H. Masson, and E. Hüllermeier, “Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty,” in Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095.
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2018 | Conference Paper | LibreCat-ID: 2109
M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018.
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2018 | Conference Paper | LibreCat-ID: 2471
F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
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2018 | Book Chapter | LibreCat-ID: 6423
D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.
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2018 | Conference Paper | LibreCat-ID: 10148
A. El Mesaoudi-Paul, E. Hüllermeier, and R. Busa-Fekete, “Ranking Distributions based on  Noisy Sorting,” in Proc. 35th Int. Conference on Machine Learning (ICML), 2018, pp. 3469–3477.
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2018 | Conference Paper | LibreCat-ID: 3552
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.
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2018 | Conference Paper | LibreCat-ID: 10149
M. Hesse, J. Timmermann, E. Hüllermeier, and A. Trächtler, “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart,” in Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 2018, pp. 15–20.
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2018 | Journal Article | LibreCat-ID: 10276
D. Schäfer and E. Hüllermeier, “Dyad Ranking Using Plackett-Luce Models based on joint feature representations,” Machine Learning, vol. 107, no. 5, pp. 903–941, 2018.
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2018 | Book Chapter | LibreCat-ID: 10783
I. Couso and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in Frontiers in Computational Intelligence, S. Mostaghim, A. Nürnberger, and C. Borgelt, Eds. Springer, 2018, pp. 31–46.
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2018 | Conference Paper | LibreCat-ID: 2857
F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 28th International Conference on Automated Planning and Scheduling, Delft, Netherlands, 2018.
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2018 | Journal Article | LibreCat-ID: 3402
V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 5693
H. Graf, Ranking of Classification Algorithms in AutoML. 2018.
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2018 | Conference Abstract | LibreCat-ID: 1379
N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M. D. Wever, and E. Hüllermeier, “Supporting the Cognitive Process in Annotation Tasks,” in Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, Stuttgart, Germany, 2018.
LibreCat | Files available | Download (ext.)
 

2018 | Conference Paper | LibreCat-ID: 10152
F. Mohr, M. D. Wever, and E. Hüllermeier, “On-the-Fly Service Construction with Prototypes,” in Proc. 15th Int. Conference on Services Computing (SCC), 2018, pp. 225–232.
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2018 | Conference Paper | LibreCat-ID: 10145
M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank Based on Analogical Reasoning,” in Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958.
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2018 | Journal Article | LibreCat-ID: 10784
F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated machine learning via hierarchical planning,” Machine Learning, vol. 107, no. 8–10, pp. 1495–1515, 2018.
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2018 | Conference Paper | LibreCat-ID: 10188
M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of evolved nested dichotomies for classificaton,” in Proc. Genetic and Evolutionary Computation Conference (GECCO), 2018, pp. 561–568.
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2018 | Journal Article | LibreCat-ID: 3510
F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” Machine Learning, 2018.
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2017 | Bachelorsthesis | LibreCat-ID: 5694
N. N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. 2017.
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2017 | Conference Paper | LibreCat-ID: 1180
M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.
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2017 | Conference Paper | LibreCat-ID: 10204
R. Ewerth et al., “Estimating relative depth in single images via rankboost,” in Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924.
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