Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.”https://doi.org/10.1007/978-3-030-88942-5__In Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021.https://doi.org/10.1007/978-3-030-88942-5 .

Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification, 2021.https://doi.org/10.17619/UNIPB/1-1302 .

Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” PAKDD, 2021.

Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.”https://doi.org/10.1007/978-3-030-58285-2_30__In Lecture Notes in Computer Science. Cham, 2020.https://doi.org/10.1007/978-3-030-58285-2_30 .

Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020.

Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International Conference on Machine Learning, 778–87, 2020.

Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.

Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In Discovery Science, 2020.

Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI 2020: Advances in Artificial Intelligence, 2020.

Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”https://doi.org/10.1007/978-3-030-66770-2_8__In Proceedings of the ECMLPKDD 2020, 2020.https://doi.org/10.1007/978-3-030-66770-2_8 .

Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities. episciences, n.d.

Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.

El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.”https://doi.org/10.1007/978-3-030-53552-0_22__In Learning and Intelligent Optimization. LION 2020., 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020.https://doi.org/10.1007/978-3-030-53552-0_22 .

El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” ArXiv:2002.04275, n.d.

Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In ACML 2020, 2020.

Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, n.d.

Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.” Springer, n.d.

Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.”https://doi.org/10.1162/evco_a_00266__Evolutionary Computation 28, no. 2 (2020): 165–193.https://doi.org/10.1162/evco_a_00266 .

Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.

Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142 (2019): 124–46.

Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019.

Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.

Tagne, V. K., S. Fotso, L. A.  Fono, and Eyke Hüllermeier. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation 15, no. 2 (2019): 191–213.

Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.”https://doi.org/10.1109/mci.2018.2881642__IEEE Computational Intelligence Magazine, 2019, 31–44.https://doi.org/10.1109/mci.2018.2881642 .

Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target Prediction: A Unifying View on Problems and Methods.”https://doi.org/10.1007/s10618-018-0595-5__Data Mining and Knowledge Discovery 33, no. 2 (2019): 293–324.https://doi.org/10.1007/s10618-018-0595-5 .

Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, and Willem 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.

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.”https://doi.org/10.1007/978-3-030-33778-0_22__In Discovery Science. Cham, 2019.https://doi.org/10.1007/978-3-030-33778-0_22 .

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning with Kernels.”https://doi.org/10.1007/978-3-030-30179-8_3__In KI 2019: Advances in Artificial Intelligence. Cham, 2019.https://doi.org/10.1007/978-3-030-30179-8_3 .

Nguyen, Vu-Linh, Sébastien Destercke, and Eyke Hüllermeier. “Epistemic Uncertainty Sampling.”https://doi.org/10.1007/978-3-030-33778-0_7__In Discovery Science. Cham, 2019.https://doi.org/10.1007/978-3-030-33778-0_7 .

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.”https://doi.org/10.1016/j.jmva.2019.02.017__In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.https://doi.org/10.1016/j.jmva.2019.02.017 .

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.”https://doi.org/10.1109/ivs.2019.8814100__In 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.https://doi.org/10.1109/ivs.2019.8814100 .

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.

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.

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.

Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.”https://doi.org/10.1145/3363572__ACM Transactions on Knowledge Discovery from Data, 2019, 1–36.https://doi.org/10.1145/3363572 .

Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.”https://doi.org/10.1016/j.jmva.2019.02.017__Journal of Multivariate Analysis, 2019, 291–312.https://doi.org/10.1016/j.jmva.2019.02.017 .

Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.”https://doi.org/10.1214/19-ejs1555__Electronic Journal of Statistics, 2019, 1523–79.https://doi.org/10.1214/19-ejs1555 .

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.

Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019.

Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rczaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.”https://doi.org/10.1109/TCDS.2019.2892991__IEEE Transactions on Cognitive and Developmental Systems, 2019.https://doi.org/10.1109/TCDS.2019.2892991 .

Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.”https://doi.org/10.1109/SCC.2018.00039__In SCC. San Francisco, CA, USA: IEEE, 2018.https://doi.org/10.1109/SCC.2018.00039 .

Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.

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.

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.

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.

Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service Construction with Prototypes.”https://doi.org/10.1109/SCC.2018.00036__In SCC. San Francisco, CA, USA: IEEE Computer Society, 2018.https://doi.org/10.1109/SCC.2018.00036 .

Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.”https://doi.org/10.1007/s10994-018-5733-1__Machine Learning, 2018.https://doi.org/10.1007/s10994-018-5733-1 .

Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.”https://doi.org/10.1007/s10994-018-5735-z__Machine Learning, 2018, 1495–1515.https://doi.org/10.1007/s10994-018-5735-z .

Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps for Multi-Class Classification.”https://doi.org/10.1007/978-3-030-01768-2_19__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 .

Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.

Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.”https://doi.org/10.1145/3205455.3205562__In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM, 2018.https://doi.org/10.1145/3205455.3205562 .

Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018.

Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine Learning Service Composition,” 2018.

Graf, Helena. Ranking of Classification Algorithms in AutoML. Universität Paderborn, 2018.

Scheibl, Manuel. Learning about Learning Curves from Dataset Properties. Universität Paderborn, 2018.

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.”https://doi.org/10.1007/978-3-030-01771-2_11__In Discovery Science, 161–75. Cham: Springer International Publishing, 2018.https://doi.org/10.1007/978-3-030-01771-2_11 .

Abiteboul, S., M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, et al., eds. Research Directions for Principles of Data Management. Vol. 7, 2018.

Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” In Frontiers in Computational Intelligence, edited by Sanaz Mostaghim, Andreas Nürnberger, and Christian Borgelt, 31–46. Springer, 2018.

Schäfer, D., and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018): 903–41.

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” In Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2951–58, 2018.

El Mesaoudi-Paul, Adil, Eyke Hüllermeier, and Robert Busa-Fekete. “Ranking Distributions Based on Noisy Sorting.” In Proc. 35th Int. Conference on Machine Learning (ICML), 3469–77. Verlagsschriftenreihe Des Heinz Nixdorf Instituts, Paderborn. Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2018.

Hesse, M., J. Timmermann, Eyke Hüllermeier, and Ansgar 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, 15–20, 2018.

Mencia, E.Loza, J. Fürnkranz, Eyke Hüllermeier, and M. Rapp. “Learning Interpretable Rules for Multi-Label Classification.” In Explainable and Interpretable Models in Computer Vision and Machine Learning, edited by H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, and M.A.J. van Gerven, 81–113. The Springer Series on Challenges in Machine Learning. Springer, 2018.

Nguyen, Vu-Linh, Sebastian Destercke, M.-H. Masson, and Eyke Hüllermeier. “Reliable Multi-Class Classification Based on Pairwise Epistemic and Aleatoric Uncertainty.” In Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–95, 2018.

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In Proc. 21st Int. Conference on Discovery Science (DS), 161–75, 2018.

Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018): 903–41.

Seemann, Nina, Michaela Geierhos, Marie-Luis Merten, Doris Tophinke, Marcel Dominik Wever, and Eyke Hüllermeier. “Supporting the Cognitive Process in Annotation Tasks.” In Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, edited by Kerstin  Eckart and Dominik  Schlechtweg, 2018.

Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Analysis of Gradient Descent Methods with Nondiminishing Bounded Errors.” IEEE Transactions on Automatic Control 63, no. 5 (2017): 1465–71.

Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions.” Mathematics of Operations Research 42, no. 3 (2017): 648–61.

Melnikov, Vitalik, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics.”https://doi.org/10.5445/KSP/1000074341__In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing, 2017.https://doi.org/10.5445/KSP/1000074341 .

Jakobs, Marie-Christine, Julia Krämer, Dirk van Straaten, and Theodor Lettmann. “Certiû01cation Matters for Service Markets.” In The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus Uhl, 7–12, 2017.

Seemann, Nina, Marie-Luis Merten, Michaela Geierhos, Doris Tophinke, and Eyke Hüllermeier. “Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German.”https://doi.org/10.18653/v1/W17-2206__In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, 40–45. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL), 2017.https://doi.org/10.18653/v1/W17-2206 .

Schnitker, Nino Noel. Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn, 2017.

Gupta, Pritha, Alexander Hetzer, Tanja Tornede, Sebastian Gottschalk, Andreas Kornelsen, Sebastian Osterbrink, Karlson Pfannschmidt, and Eyke Hüllermeier. “JPL: A Java-Based Software Framework for Preference Learning,” 2017.

Hetzer, Alexander, and Tanja Tornede. Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction. Universität Paderborn, 2017.

Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.”https://doi.org/10.1145/3121257.3121262__In Proceedings of the 3rd International Workshop on Software Analytics, 23–26. SWAN’17, 2017.https://doi.org/10.1145/3121257.3121262 .

Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. Predicting Rankings of Software Verification Competitions, 2017.

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, 1000–1005, 2017.

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, 107:1000–1005. Springer, 2017.

Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop Computational Intelligence. Dortmund, 2017.

Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies. A Comparison of Two Heuristics.” In In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann, Eyke Hüllermeier, and R. Mikut, 1–12. KIT Scientific Publishing, 2017.

Czech, M., Eyke Hüllermeier, M.C. Jacobs, and Heike Wehrheim. “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, 2017.

Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation and Coarse Data.” In In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, 3–16. Springer, 2017.

Ewerth, Ralph, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok, K. Dembczynski, and Eyke Hüllermeier. “Estimating Relative Depth in Single Images via Rankboost.” In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 919–24, 2017.

Ahmadi Fahandar, Mohsen, Eyke Hüllermeier, and Ines Couso. “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening.” In Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 1078–87, 2017.

Mohr, Felix, Theodor Lettmann, and Eyke Hüllermeier. “Planning with Independent Task Networks.”https://doi.org/10.1007/978-3-319-67190-1_15__In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 193–206, 2017.https://doi.org/10.1007/978-3-319-67190-1_15 .

Czech, M., Eyke Hüllermeier, M.-C. Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 23–26, 2017.

Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation and Coarse Data.” In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 3–16, 2017.

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017.

Hoffmann, F., Eyke Hüllermeier, and R. Mikut. “(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017,” 2017.

Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics.” In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017, 1–12, 2017.

Shaker, Ammar, W. Heldt, and Eyke Hüllermeier. “Learning TSK Fuzzy Rules from Data Streams.” In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia, 2017.

Bräuning, M., Eyke Hüllermeier, T. Keller, and M. Glaum. “Lexicographic Preferences for Predictive Modeling of Human Decision Making. A New Machine Learning Method with an Application  in Accounting.” European Journal of Operational Research 258, no. 1 (2017): 295–306.

Platenius, M.-C., Ammar Shaker, M. Becker, Eyke Hüllermeier, and W. Schäfer. “Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic.” IEEE Transactions on Software Engineering 43, no. 8 (2017): 739–59.

Hüllermeier, Eyke. “From Knowledge-Based to Data-Driven Modeling of Fuzzy Rule-Based Systems: A Critical Reflection.” The Computing Research Repository  (CoRR), 2017.

Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stochastic Recursive Inclusion in Two Timescales with an Application to the Lagrangian Dual Problem.” Stochastics 88, no. 8 (2016): 1173–87.

Melnikov, Vitalik, Eyke Hüllermeier, Daniel Kaimann, Berndhttps://doi.org/10.4467/20838476si.16.006.6187__Frick, andhttps://doi.org/10.4467/20838476si.16.006.6187__Prithahttps://doi.org/10.4467/20838476si.16.006.6187__Gupta. “Pairwise versus Pointwise Ranking: A Case Study.”https://doi.org/10.4467/20838476si.16.006.6187__Schedae Informaticae 25 (2016).https://doi.org/10.4467/20838476si.16.006.6187 .

Platenius, Marie Christin, Ammar Shaker, Matthias Becker, Eyke Hüllermeier, and Wilhelm Schäfer. “Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic.”https://doi.org/10.1109/TSE.2016.2632115__IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, no. 8 (2016): 739–59.https://doi.org/10.1109/TSE.2016.2632115 .

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.”https://doi.org/10.1007/978-3-319-46227-1_47__In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 756–71. LNCS, 2016.https://doi.org/10.1007/978-3-319-46227-1_47 .

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb. Springer, 2016.