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449 Publications


2019 | Conference Paper | LibreCat-ID: 15009
Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries
N. Epple, S. Dari, L. Drees, V. Protschky, A. Riener, in: 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.
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2019 | Conference Paper | LibreCat-ID: 15011 | OA
Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking
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.
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2019 | Conference Paper | LibreCat-ID: 15013
A Reduction of Label Ranking to Multiclass Classification
K. Brinker, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Würzburg, Germany, 2019.
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2019 | Conference Paper | LibreCat-ID: 15014
Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants
E. Hüllermeier, I. Couso, S. Diestercke, in: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.
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2019 | Journal Article | LibreCat-ID: 15015
Mining Rank Data
S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from Data (2019) 1–36.
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2019 | Journal Article | LibreCat-ID: 14027
Asymptotic confidence sets for the jump curve in bivariate regression problems
V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312.
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2019 | Journal Article | LibreCat-ID: 14028
Adaptive confidence sets for kink estimation
V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.
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2019 | Conference Abstract | LibreCat-ID: 13132
From Automated to On-The-Fly Machine Learning
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.
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2019 | Conference Paper | LibreCat-ID: 10232 | OA
Automating Multi-Label Classification Extending ML-Plan
M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.
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2019 | Journal Article | LibreCat-ID: 20243
Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches
K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).
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2018 | Conference Paper | LibreCat-ID: 2479 | OA
(WIP) Towards the Automated Composition of Machine Learning Services
F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018.
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2018 | Preprint | LibreCat-ID: 19524
Deep Architectures for Learning Context-dependent Ranking Functions
K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).
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2018 | Conference Paper | LibreCat-ID: 2857 | OA
Programmatic Task Network Planning
F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.
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2018 | Journal Article | LibreCat-ID: 24150
Stability of stochastic approximations with “controlled markov” noise and temporal difference learning
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.
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2018 | Journal Article | LibreCat-ID: 24151
Deepcas: A deep reinforcement learning algorithm for control-aware scheduling
B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters 2 (2018) 737–742.
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2018 | Conference Paper | LibreCat-ID: 2471 | OA
On-The-Fly Service Construction with Prototypes
F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.
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2018 | Journal Article | LibreCat-ID: 3402 LibreCat | Files available | DOI
 

2018 | Journal Article | LibreCat-ID: 3510 | OA
ML-Plan: Automated Machine Learning via Hierarchical Planning
F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
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2018 | Conference Paper | LibreCat-ID: 3552 | OA
Reduction Stumps for Multi-Class Classification
F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.
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2018 | Conference Paper | LibreCat-ID: 3852 | OA
ML-Plan for Unlimited-Length Machine Learning Pipelines
M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.
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2018 | Conference Paper | LibreCat-ID: 2109 | OA
Ensembles of Evolved Nested Dichotomies for Classification
M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.
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2018 | Preprint | LibreCat-ID: 17713 | OA
Automated Multi-Label Classification based on ML-Plan
M.D. Wever, F. Mohr, E. Hüllermeier, (2018).
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2018 | Preprint | LibreCat-ID: 17714 | OA
Automated machine learning service composition
F. Mohr, M.D. Wever, E. Hüllermeier, (2018).
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2018 | Bachelorsthesis | LibreCat-ID: 5693
Ranking of Classification Algorithms in AutoML
H. Graf, Ranking of Classification Algorithms in AutoML, Universität Paderborn, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Learning about learning curves from dataset properties
M. Scheibl, Learning about Learning Curves from Dataset Properties, Universität Paderborn, 2018.
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2018 | Book Chapter | LibreCat-ID: 6423
Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International Publishing, Cham, 2018, pp. 161–175.
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2018 | Conference (Editor) | LibreCat-ID: 10591
Research Directions for Principles of Data Management
S. Abiteboul, M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, E. Hüllermeier, B. Kimelfeld, L. Libkin, W. Martens, T. Milo, F. Murlak, F. Neven, M. Ortiz, T. Schwentick, J. Stoyanovich, J. Su, D. Suciu, V. Vianu, K. Yi, eds., Research Directions for Principles of Data Management, 2018.
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2018 | Book Chapter | LibreCat-ID: 10783
Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, E. Hüllermeier, in: S. Mostaghim, A. Nürnberger, C. Borgelt (Eds.), Frontiers in Computational Intelligence, Springer, 2018, pp. 31–46.
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2018 | Journal Article | LibreCat-ID: 16038
Dyad ranking using Plackett-Luce models based on joint feature representations
D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.
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2018 | Conference Paper | LibreCat-ID: 10145
Learning to Rank Based on Analogical Reasoning
M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958.
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2018 | Conference Paper | LibreCat-ID: 10148
Ranking Distributions based on Noisy Sorting
A. El Mesaoudi-Paul, E. Hüllermeier, R. Busa-Fekete, in: Proc. 35th Int. Conference on Machine Learning (ICML), Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2018, pp. 3469–3477.
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2018 | Conference Paper | LibreCat-ID: 10149
A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart
M. Hesse, J. Timmermann, E. Hüllermeier, A. Trächtler, 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 | Book Chapter | LibreCat-ID: 10152
Learning interpretable rules for multi-label classification
E.L. Mencia, J. Fürnkranz, E. Hüllermeier, M. Rapp, 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, Springer, 2018, pp. 81–113.
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2018 | Conference Paper | LibreCat-ID: 10181
Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty
V.-L. Nguyen, S. Destercke, M.-H. Masson, E. Hüllermeier, in: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095.
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2018 | Conference Paper | LibreCat-ID: 10184
Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175.
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2018 | Journal Article | LibreCat-ID: 10276
Dyad Ranking Using Plackett-Luce Models based on joint feature representations
D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.
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2018 | Conference Abstract | LibreCat-ID: 1379 | OA
Supporting the Cognitive Process in Annotation Tasks
N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M.D. Wever, E. Hüllermeier, in: K. Eckart, D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, 2018.
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2017 | Journal Article | LibreCat-ID: 24152
Analysis of gradient descent methods with nondiminishing bounded errors
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 63 (2017) 1465–1471.
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2017 | Journal Article | LibreCat-ID: 24153
A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions
A. Ramaswamy, S. Bhatnagar, Mathematics of Operations Research 42 (2017) 648–661.
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2017 | Conference Paper | LibreCat-ID: 3325
Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics
V. Melnikov, E. Hüllermeier, in: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, KIT Scientific Publishing, 2017.
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2017 | Conference Paper | LibreCat-ID: 115
Certification Matters for Service Markets
M.-C. Jakobs, J. Krämer, D. van Straaten, T. Lettmann, in: T.P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.
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2017 | Conference Paper | LibreCat-ID: 1158
Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German
N. Seemann, M.-L. Merten, M. Geierhos, D. Tophinke, E. Hüllermeier, in: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Association for Computational Linguistics (ACL), Stroudsburg, PA, USA, 2017, pp. 40–45.
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2017 | Bachelorsthesis | LibreCat-ID: 5694
Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies
N.N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies, Universität Paderborn, 2017.
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2017 | Conference Abstract | LibreCat-ID: 5722
jPL: A Java-based Software Framework for Preference Learning
P. Gupta, A. Hetzer, T. Tornede, S. Gottschalk, A. Kornelsen, S. Osterbrink, K. Pfannschmidt, E. Hüllermeier, in: 2017.
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2017 | Mastersthesis | LibreCat-ID: 5724
Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction
A. Hetzer, T. Tornede, Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction, Universität Paderborn, 2017.
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2017 | Conference Paper | LibreCat-ID: 71
Predicting Rankings of Software Verification Tools
M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.
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2017 | Report | LibreCat-ID: 72
Predicting Rankings of Software Verification Competitions
M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings of Software Verification Competitions, 2017.
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2017 | Encyclopedia Article | LibreCat-ID: 10589
Preference Learning
J. Fürnkranz, E. Hüllermeier, in: Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.
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2017 | Book Chapter | LibreCat-ID: 10784
Preference Learning
J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2017, pp. 1000–1005.
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2017 | Conference Paper | LibreCat-ID: 1180 | OA
Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization
M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.
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