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.
LibreCat: /record/15009
DOI: 10.1109/ivs.2019.8814100

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.
LibreCat: /record/15011

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.
LibreCat: /record/15013

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.
LibreCat: /record/15014

S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on Knowledge Discovery from Data, pp. 1–36, 2019.
LibreCat: /record/15015
DOI: 10.1145/3363572

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.
LibreCat: /record/14027
DOI: 10.1016/j.jmva.2019.02.017

V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” Electronic Journal of Statistics, pp. 1523–1579, 2019.
LibreCat: /record/14028
DOI: 10.1214/19-ejs1555

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.
LibreCat: /record/13132

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.
LibreCat: /record/10232

K. Rohlfing, G. Leonardi, I. Nomikou, J. Rczaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,”10.1109/TCDS.2019.2892991__IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991 .
LibreCat: /record/20243
DOI: 10.1109/TCDS.2019.2892991

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.
LibreCat: /record/2479
DOI: 10.1109/SCC.2018.00039

K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” arXiv:1803.05796. 2018.
LibreCat: /record/19524

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.
LibreCat: /record/2857

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.
LibreCat: /record/24150

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.
LibreCat: /record/24151

F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
LibreCat: /record/2471
DOI: 10.1109/SCC.2018.00036

V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018.
LibreCat: /record/3402
DOI: 10.1007/s10994-018-5733-1

F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,”10.1007/s10994-018-5735-z__Machine Learning, pp. 1495–1515, 2018, doi:10.1007/s10994-018-5735-z .
LibreCat: /record/3510
DOI: 10.1007/s10994-018-5735-z

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.
LibreCat: /record/3552
DOI: 10.1007/978-3-030-01768-2_19

M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
LibreCat: /record/3852

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.
LibreCat: /record/2109
DOI: 10.1145/3205455.3205562

M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.
LibreCat: /record/17713

F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.
LibreCat: /record/17714

H. Graf, Ranking of Classification Algorithms in AutoML. Universität Paderborn, 2018.
LibreCat: /record/5693

M. Scheibl, Learning about learning curves from dataset properties. Universität Paderborn, 2018.
LibreCat: /record/5936

D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.
LibreCat: /record/6423
DOI: 10.1007/978-3-030-01771-2_11

S. Abiteboul et al., Eds., Research Directions for Principles of Data Management, vol. 7, no. 1. 2018, pp. 1–29.
LibreCat: /record/10591

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.
LibreCat: /record/10783

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.
LibreCat: /record/16038

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.
LibreCat: /record/10145

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.
LibreCat: /record/10148

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.
LibreCat: /record/10149

E. L. Mencia, J. Fürnkranz, E. Hüllermeier, and M. Rapp, “Learning interpretable rules for multi-label classification,” in Explainable and Interpretable Models in Computer Vision and Machine Learning, H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, and M. A. J. van Gerven, Eds. Springer, 2018, pp. 81–113.
LibreCat: /record/10152

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.
LibreCat: /record/10181

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.
LibreCat: /record/10184

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.
LibreCat: /record/10276

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: /record/1379

A. Ramaswamy and S. Bhatnagar, “Analysis of gradient descent methods with nondiminishing bounded errors,” IEEE Transactions on Automatic Control, vol. 63, no. 5, pp. 1465–1471, 2017.
LibreCat: /record/24152

A. Ramaswamy and S. Bhatnagar, “A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions,” Mathematics of Operations Research, vol. 42, no. 3, pp. 648–661, 2017.
LibreCat: /record/24153

V. Melnikov and E. Hüllermeier, “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics,” in Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, 2017.
LibreCat: /record/3325
DOI: 10.5445/KSP/1000074341

M.-C. Jakobs, J. Krämer, D. van Straaten, and T. Lettmann, “Certiû01cation Matters for Service Markets,” in The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.
LibreCat: /record/115

N. Seemann, M.-L. Merten, M. Geierhos, D. Tophinke, and E. Hüllermeier, “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, Vancouver, BC, Canada, 2017, pp. 40–45.
LibreCat: /record/1158
DOI: 10.18653/v1/W17-2206

N. N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn, 2017.
LibreCat: /record/5694

P. Gupta et al., “jPL: A Java-based Software Framework for Preference Learning,” presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock, 2017.
LibreCat: /record/5722

A. Hetzer and T. Tornede, Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn, 2017.
LibreCat: /record/5724

M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings of Software Verification Tools,” in Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.
LibreCat: /record/71
DOI: 10.1145/3121257.3121262

M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, Predicting Rankings of Software Verification Competitions. 2017.
LibreCat: /record/72

J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.
LibreCat: /record/10589

J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, vol. 107, C. Sammut and G. I. Webb, Eds. Springer, 2017, pp. 1000–1005.
LibreCat: /record/10784

M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.
LibreCat: /record/1180