Uniform approximation in classical weak convergence theory
V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).

Towards Automated Machine Learning for Multi-Label Classification
M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.

Choice Functions Generated by Mallows and Plackett–Luce Relations
V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213.

Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning
I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence Magazine (2019) 31–44.

Multi-target prediction: a unifying view on problems and methods
W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324.

Set-Valued Prediction in Multi-Class Classification
T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, 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.

Feature Selection for Analogy-Based Learning to Rank
M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.

Analogy-Based Preference Learning with Kernels
M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial Intelligence, Cham, 2019.

Epistemic Uncertainty Sampling
V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham, 2019.

Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA
V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.

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.

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.

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.

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.

Mining Rank Data
S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from Data (2019) 1–36.

Asymptotic confidence sets for the jump curve in bivariate regression problems
V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312.

Adaptive confidence sets for kink estimation
V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.

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.

Automating Multi-Label Classification Extending ML-Plan
M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.

Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches
K. Rohlfing, G. Leonardi, I. Nomikou, J. Rczaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).

(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.

Deep Architectures for Learning Context-dependent Ranking Functions
K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).

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.

Stability of stochastic approximations with ccontrolled markovd noise and temporal difference learning
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.

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.

On-The-Fly Service Construction with Prototypes
F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.

On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis
V. Melnikov, E. Hüllermeier, Machine Learning (2018).

ML-Plan: Automated Machine Learning via Hierarchical Planning
F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.

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.

ML-Plan for Unlimited-Length Machine Learning Pipelines
M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.

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.

Automated Multi-Label Classification based on ML-Plan
M.D. Wever, F. Mohr, E. Hüllermeier, (2018).

Automated machine learning service composition
F. Mohr, M.D. Wever, E. Hüllermeier, (2018).

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

Learning about learning curves from dataset properties
M. Scheibl, Learning about Learning Curves from Dataset Properties, Universität Paderborn, 2018.

Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International Publishing, Cham, 2018, pp. 161–175.

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.

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.

Dyad ranking using Plackett-Luce models based on joint feature representations
D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.

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.

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.

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.

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.

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.

Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175.

Dyad Ranking Using Plackett-Luce Models based on joint feature representations
D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.

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.

Analysis of gradient descent methods with nondiminishing bounded errors
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 63 (2017) 1465–1471.

A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions
A. Ramaswamy, S. Bhatnagar, Mathematics of Operations Research 42 (2017) 648–661.

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.

Certiû01cation 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.

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.

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

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.

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.

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.

Predicting Rankings of Software Verification Competitions
M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings of Software Verification Competitions, 2017.

Preference Learning
J. Fürnkranz, E. Hüllermeier, in: Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.

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.

Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization
M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.

Optimizing the structure of nested dichotomies. A comparison of two heuristics
V. Melnikov, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2017, pp. 1–12.

Predicting rankings of software verification tools
M. Czech, E. Hüllermeier, M.C. Jacobs, H. Wehrheim, in: In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017.

Maximum likelihood estimation and coarse data
I. Couso, D. Dubois, E. Hüllermeier, in: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017, pp. 3–16.

Estimating relative depth in single images via rankboost
R. Ewerth, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok, K. Dembczynski, E. Hüllermeier, in: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924.

Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening
M. Ahmadi Fahandar, E. Hüllermeier, I. Couso, in: Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.

Planning with Independent Task Networks
F. Mohr, T. Lettmann, E. Hüllermeier, in: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206.

Predicting rankings of software verification tools
M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.

Maximum Likelihood Estimation and Coarse Data
I. Couso, D. Dubois, E. Hüllermeier, in: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16.

Learning to Rank based on Analogical Reasoning
M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017.

(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017
F. Hoffmann, E. Hüllermeier, R. Mikut, in: 2017.

Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics
V. Melnikov, E. Hüllermeier, in: Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017, 2017, pp. 1–12.

Learning TSK Fuzzy Rules from Data Streams
A. Shaker, W. Heldt, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia, 2017.

Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting
M. Bräuning, E. Hüllermeier, T. Keller, M. Glaum, European Journal of Operational Research 258 (2017) 295–306.

Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic
M.-C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, W. Schäfer, IEEE Transactions on Software Engineering 43 (2017) 739–759.

From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection
E. Hüllermeier, The Computing Research Repository  (CoRR) (2017).

Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem
A. Ramaswamy, S. Bhatnagar, Stochastics 88 (2016) 1173–1187.

Pairwise versus Pointwise Ranking: A Case Study
V. Melnikov, E. Hüllermeier, D. Kaimann, B. Frick,  Pritha  Gupta, Schedae Informaticae 25 (2016).

Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic
M.C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, W. Schäfer, IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017 (2016) 739–759.

Learning to Aggregate Using Uninorms
V. Melnikov, E. Hüllermeier, in: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.

Preference Learning
J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2016.

On the identifiability of models  in multi-criteria preference learning
C. Labreuche, E. Hüllermeier, P. Vojtas, A. Fallah Tehrani, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), In Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, 2016.

Preference -based reinforcement learning using dyad ranking
D. Schäfer, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), In Proceedings DPL16 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany, 2016.

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, M. Ahmadi Fahandar, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), In Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, 2016.

Support vector classification on noisy data using fuzzy superset losses
S. Lu, E. Hüllermeier, in: E. Hüllermeier, F. Hoffmann, R. Mikut (Eds.), In Proceedings 26th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2016, pp. 1–8.

Plackett-Luce networks for dyad ranking
D. Schäfer, E. Hüllermeier, in: In Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany, 2016.

Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts
K. Pfannschmidt, E. Hüllermeier, S. Held, R. Neiger, in: In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–461.

CavSimBase: A database for large scale comparison of protein binding sites
M. Leinweber, T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, E. Hüllermeier, IEEE Transactions on Knowledge and Data Engineering 28 (2016) 1423–1434.

Towards Automated Service Composition Under Quality Constraints
F. Mohr, Towards Automated Service Composition Under Quality Constraints, Universität Paderborn, 2016.

Preference Learning
J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2016.

 Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany
F. Hoffmann, E. Hüllermeier, R. Mikut, eds.,  Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany, 2016.

Extreme F-measure maximization using sparse probability estimates 
K. Jasinska, K. Dembczynski, R. Busa-Fekete, T. Klerx, E. Hüllermeier, in: M.F. Balcan, K.Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, 2016.

Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016
V. Melnikov, E. Hüllermeier, in: European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 756–771.

Consistency of probalistic classifier trees
K. Dembczynski, W. Kotlowski, W. Waegeman, R. Busa-Fekete, E. Hüllermeier, in: In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 511–526.

Predicting the electricity consumption of buildings: An improved CBR approach
A. Shabani, A. Paul, R. Platon, E. Hüllermeier, in: In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 2016, pp. 356–369.

Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts
K. Pfannschmidt, E. Hüllermeier, S. Held, R. Neiger, in: In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–461.

On the Identifiability of models in multi-criteria preference learning 
C. Labreuche, E. Hüllermeier, P. Vojtas, A. Fallah Tehrani, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, M. Ahmadi Fahandar, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Support vector classification on noisy data using fuzzy supersets losses
S. Lu, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, 2016, pp. 1–8.

Plackett-Luce networks for dyad ranking
D. Schäfer, E. Hüllermeier, in: In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.