Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.https://doi.org/10.17619/UNIPB/1-1780  .
LibreCat: /record/45780
DOI: 10.17619/UNIPB/1-1780 

Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.”https://doi.org/10.1137/21M1412682__SIAM Journal on Scientific Computing 45, no. 2 (2023): 9–95.https://doi.org/10.1137/21M1412682 .
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence,” 2021.
LibreCat: /record/24382

Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.”https://doi.org/10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1.https://doi.org/10.1109/tpami.2021.3051276 .
LibreCat: /record/21004
DOI: 10.1109/tpami.2021.3051276

Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d.
LibreCat: /record/21092

Bengs, Viktor, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research 22, no. 7 (2021): 1–108.
LibreCat: /record/21535

Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference, 2021.
LibreCat: /record/21570

Bernijazov, Ruslan, Alexander Dicks, Roman Dumitrescu, Marc Foullois, Jonas Manuel Hanselle, Eyke Hüllermeier, Gökce Karakaya, et al. “A Meta-Review on Artiû01cial Intelligence in Product Creation.” In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.
LibreCat: /record/23779

Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021.
LibreCat: /record/22913

Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021.
LibreCat: /record/22914

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 .
LibreCat: /record/27381
DOI: 10.1007/978-3-030-88942-5
arXiv: 2104.08869

Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification, 2021.https://doi.org/10.17619/UNIPB/1-1302 .
LibreCat: /record/27284
DOI: 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.
LibreCat: /record/21198

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 .
LibreCat: /record/19521
DOI: 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.
LibreCat: /record/19953
arXiv: 2007.00346

Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International Conference on Machine Learning, 778–87, 2020.
LibreCat: /record/21534

Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.
LibreCat: /record/21536

Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In Discovery Science, 2020.
LibreCat: /record/17407

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.
LibreCat: /record/17408

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 .
LibreCat: /record/17424
DOI: 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.
LibreCat: /record/17605

Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
LibreCat: /record/20306

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 .
LibreCat: /record/18014
DOI: 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.
LibreCat: /record/18017

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.
LibreCat: /record/18276

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.
LibreCat: /record/16725

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.
LibreCat: /record/15629

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 .
LibreCat: /record/15025
DOI: 10.1162/evco_a_00266

Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.
LibreCat: /record/19523

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.
LibreCat: /record/17565

Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019.
LibreCat: /record/18018

Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.
LibreCat: /record/8868

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.
LibreCat: /record/10578

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 .
LibreCat: /record/15001
DOI: 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 .
LibreCat: /record/15002
DOI: 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.
LibreCat: /record/15003

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 .
LibreCat: /record/15004
DOI: 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 .
LibreCat: /record/15005
DOI: 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 .
LibreCat: /record/15006
DOI: 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 .
LibreCat: /record/15007
DOI: 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 .
LibreCat: /record/15009
DOI: 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.
LibreCat: /record/15011

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.
LibreCat: /record/15013

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.
LibreCat: /record/15014

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 .
LibreCat: /record/15015
DOI: 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 .
LibreCat: /record/14027
DOI: 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 .
LibreCat: /record/14028
DOI: 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.
LibreCat: /record/13132

Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019.
LibreCat: /record/10232

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 .
LibreCat: /record/20243
DOI: 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 .
LibreCat: /record/2479
DOI: 10.1109/SCC.2018.00039

Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.
LibreCat: /record/19524

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.
LibreCat: /record/2857

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.
LibreCat: /record/24150

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.
LibreCat: /record/24151

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 .
LibreCat: /record/2471
DOI: 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 .
LibreCat: /record/3402
DOI: 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 .
LibreCat: /record/3510
DOI: 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 .
LibreCat: /record/3552
DOI: 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.
LibreCat: /record/3852

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

Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018.
LibreCat: /record/17713

Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine Learning Service Composition,” 2018.
LibreCat: /record/17714

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

Scheibl, Manuel. Learning about Learning Curves from Dataset Properties. Universität Paderborn, 2018.
LibreCat: /record/5936

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 .
LibreCat: /record/6423
DOI: 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.
LibreCat: /record/10591

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.
LibreCat: /record/10783

Schäfer, D., and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018): 903–41.
LibreCat: /record/16038

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.
LibreCat: /record/10145

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.
LibreCat: /record/10148

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.
LibreCat: /record/10149

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.
LibreCat: /record/10152

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.
LibreCat: /record/10181

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In Proc. 21st Int. Conference on Discovery Science (DS), 161–75, 2018.
LibreCat: /record/10184

Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018): 903–41.
LibreCat: /record/10276

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.
LibreCat: /record/1379

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.
LibreCat: /record/24152

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.
LibreCat: /record/24153

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 .
LibreCat: /record/3325
DOI: 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.
LibreCat: /record/115

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 .
LibreCat: /record/1158
DOI: 10.18653/v1/W17-2206

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

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.
LibreCat: /record/5722

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

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 .
LibreCat: /record/71
DOI: 10.1145/3121257.3121262

Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. Predicting Rankings of Software Verification Competitions, 2017.
LibreCat: /record/72

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

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.
LibreCat: /record/10784

Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop Computational Intelligence. Dortmund, 2017.
LibreCat: /record/1180

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.
LibreCat: /record/15397

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.
LibreCat: /record/15399

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.
LibreCat: /record/15110

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.
LibreCat: /record/10204

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.
LibreCat: /record/10205

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 .
LibreCat: /record/10206
DOI: 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.
LibreCat: /record/10207

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.
LibreCat: /record/10208

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017.
LibreCat: /record/10209

Hoffmann, F., Eyke Hüllermeier, and R. Mikut. “(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017,” 2017.
LibreCat: /record/10212