TY - CONF AU - El Mesaoudi-Paul, Adil AU - Hüllermeier, Eyke AU - Busa-Fekete, Robert ID - 10148 T2 - Proc. 35th Int. Conference on Machine Learning (ICML) TI - Ranking Distributions based on Noisy Sorting ER - TY - CONF AU - Hesse, M. AU - Timmermann, J. AU - Hüllermeier, Eyke AU - Trächtler, Ansgar ID - 10149 T2 - Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24 TI - A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart ER - TY - CHAP AU - Mencia, E.Loza AU - Fürnkranz, J. AU - Hüllermeier, Eyke AU - Rapp, M. ED - Jair Escalante, H. ED - Escalera, S. ED - Guyon, I. ED - Baro, X. ED - Güclüütürk, Y. ED - Güclü, U. ED - van Gerven, M.A.J. ID - 10152 T2 - Explainable and Interpretable Models in Computer Vision and Machine Learning TI - Learning interpretable rules for multi-label classification ER - TY - CONF AU - Nguyen, Vu-Linh AU - Destercke, Sebastian AU - Masson, M.-H. AU - Hüllermeier, Eyke ID - 10181 T2 - Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI) TI - Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty ER - TY - CONF AU - Schäfer, Dirk AU - Hüllermeier, Eyke ID - 10184 T2 - Proc. 21st Int. Conference on Discovery Science (DS) TI - Preference-Based Reinforcement Learning Using Dyad Ranking ER - TY - JOUR AU - Schäfer, Dirk AU - Hüllermeier, Eyke ID - 10276 IS - 5 JF - Machine Learning TI - Dyad Ranking Using Plackett-Luce Models based on joint feature representations VL - 107 ER - TY - GEN AU - Seemann, Nina AU - Geierhos, Michaela AU - Merten, Marie-Luis AU - Tophinke, Doris AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ED - Eckart, Kerstin ED - Schlechtweg, Dominik ID - 1379 T2 - Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft TI - Supporting the Cognitive Process in Annotation Tasks ER - TY - JOUR AU - Ramaswamy, Arunselvan AU - Bhatnagar, Shalabh ID - 24152 IS - 5 JF - IEEE Transactions on Automatic Control TI - Analysis of gradient descent methods with nondiminishing bounded errors VL - 63 ER - TY - JOUR AU - Ramaswamy, Arunselvan AU - Bhatnagar, Shalabh ID - 24153 IS - 3 JF - Mathematics of Operations Research TI - A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions VL - 42 ER - TY - CONF AU - Melnikov, Vitalik AU - Hüllermeier, Eyke ID - 3325 T2 - Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017 TI - Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics ER - TY - CONF AB - Whenever customers have to decide between different instances of the same product, they are interested in buying the best product. In contrast, companies are interested in reducing the construction effort (and usually as a consequence thereof, the quality) to gain profit. The described setting is widely known as opposed preferences in quality of the product and also applies to the context of service-oriented computing. In general, service-oriented computing emphasizes the construction of large software systems out of existing services, where services are small and self-contained pieces of software that adhere to a specified interface. Several implementations of the same interface are considered as several instances of the same service. Thereby, customers are interested in buying the best service implementation for their service composition wrt. to metrics, such as costs, energy, memory consumption, or execution time. One way to ensure the service quality is to employ certificates, which can come in different kinds: Technical certificates proving correctness can be automatically constructed by the service provider and again be automatically checked by the user. Digital certificates allow proof of the integrity of a product. Other certificates might be rolled out if service providers follow a good software construction principle, which is checked in annual audits. Whereas all of these certificates are handled differently in service markets, what they have in common is that they influence the buying decisions of customers. In this paper, we review state-of-the-art developments in certification with respect to service-oriented computing. We not only discuss how certificates are constructed and handled in service-oriented computing but also review the effects of certificates on the market from an economic perspective. AU - Jakobs, Marie-Christine AU - Krämer, Julia AU - van Straaten, Dirk AU - Lettmann, Theodor ED - Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz ID - 115 T2 - The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) TI - Certification Matters for Service Markets ER - TY - CONF AB - In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA – a web-based annotation tool for historical and other non-standard language data – to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations. AU - Seemann, Nina AU - Merten, Marie-Luis AU - Geierhos, Michaela AU - Tophinke, Doris AU - Hüllermeier, Eyke ID - 1158 T2 - Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature TI - Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German ER - TY - GEN AU - Schnitker, Nino Noel ID - 5694 TI - Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies ER - TY - GEN AU - Gupta, Pritha AU - Hetzer, Alexander AU - Tornede, Tanja AU - Gottschalk, Sebastian AU - Kornelsen, Andreas AU - Osterbrink, Sebastian AU - Pfannschmidt, Karlson AU - Hüllermeier, Eyke ID - 5722 TI - jPL: A Java-based Software Framework for Preference Learning ER - TY - GEN AU - Hetzer, Alexander AU - Tornede, Tanja ID - 5724 TI - Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction ER - TY - CONF AB - Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6). AU - Czech, Mike AU - Hüllermeier, Eyke AU - Jakobs, Marie-Christine AU - Wehrheim, Heike ID - 71 T2 - Proceedings of the 3rd International Workshop on Software Analytics TI - Predicting Rankings of Software Verification Tools ER - TY - GEN AB - Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency. Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of verification tools on a given verification task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on verification tasks. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). AU - Czech, Mike AU - Hüllermeier, Eyke AU - Jakobs, Marie-Christine AU - Wehrheim, Heike ID - 72 TI - Predicting Rankings of Software Verification Competitions ER - TY - GEN AU - Fürnkranz, J. AU - Hüllermeier, Eyke ID - 10589 T2 - Encyclopedia of Machine Learning and Data Mining TI - Preference Learning ER - TY - CHAP AU - Fürnkranz, J. AU - Hüllermeier, Eyke ED - Sammut, C. ED - Webb, G.I. ID - 10784 T2 - Encyclopedia of Machine Learning and Data Mining TI - Preference Learning VL - 107 ER - TY - CONF AB - These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings. AU - Wever, Marcel Dominik AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 1180 T2 - 27th Workshop Computational Intelligence TI - Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization ER -