TY - CONF AU - Mohr, Felix AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke AU - Faez, Amin ID - 2479 T2 - SCC TI - (WIP) Towards the Automated Composition of Machine Learning Services ER - TY - GEN AB - Object ranking is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects, which are typically represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. Current approaches commonly focus on ranking by scoring, i.e., on learning an underlying latent utility function that seeks to capture the inherent utility of each object. These approaches, however, are not able to take possible effects of context-dependence into account, where context-dependence means that the utility or usefulness of an object may also depend on what other objects are available as alternatives. In this paper, we formalize the problem of context-dependent ranking and present two general approaches based on two natural representations of context-dependent ranking functions. Both approaches are instantiated by means of appropriate neural network architectures, which are evaluated on suitable benchmark task. AU - Pfannschmidt, Karlson AU - Gupta, Pritha AU - Hüllermeier, Eyke ID - 19524 T2 - arXiv:1803.05796 TI - Deep Architectures for Learning Context-dependent Ranking Functions ER - TY - CONF AU - Mohr, Felix AU - Lettmann, Theodor AU - Hüllermeier, Eyke AU - Wever, Marcel Dominik ID - 2857 T2 - Proceedings of the 1st ICAPS Workshop on Hierarchical Planning TI - Programmatic Task Network Planning ER - TY - JOUR AU - Ramaswamy, Arunselvan AU - Bhatnagar, Shalabh ID - 24150 IS - 6 JF - IEEE Transactions on Automatic Control TI - Stability of stochastic approximations with “controlled markov” noise and temporal difference learning VL - 64 ER - TY - JOUR AU - Demirel, Burak AU - Ramaswamy, Arunselvan AU - Quevedo, Daniel E AU - Karl, Holger ID - 24151 IS - 4 JF - IEEE Control Systems Letters TI - Deepcas: A deep reinforcement learning algorithm for control-aware scheduling VL - 2 ER - TY - CONF AU - Mohr, Felix AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ID - 2471 T2 - SCC TI - On-The-Fly Service Construction with Prototypes ER - TY - JOUR AB - In machine learning, so-called nested dichotomies are utilized as a reduction technique, i.e., to decompose a multi-class classification problem into a set of binary problems, which are solved using a simple binary classifier as a base learner. The performance of the (multi-class) classifier thus produced strongly depends on the structure of the decomposition. In this paper, we conduct an empirical study, in which we compare existing heuristics for selecting a suitable structure in the form of a nested dichotomy. Moreover, we propose two additional heuristics as natural completions. One of them is the Best-of-K heuristic, which picks the (presumably) best among K randomly generated nested dichotomies. Surprisingly, and in spite of its simplicity, it turns out to outperform the state of the art. AU - Melnikov, Vitalik AU - Hüllermeier, Eyke ID - 3402 JF - Machine Learning SN - 1573-0565 TI - On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis ER - TY - JOUR AB - Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches. AU - Mohr, Felix AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ID - 3510 JF - Machine Learning KW - AutoML KW - Hierarchical Planning KW - HTN planning KW - ML-Plan SN - 0885-6125 TI - ML-Plan: Automated Machine Learning via Hierarchical Planning ER - TY - CONF AU - Mohr, Felix AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ID - 3552 T2 - Proceedings of the Symposium on Intelligent Data Analysis TI - Reduction Stumps for Multi-Class Classification ER - TY - CONF AB - In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated. Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality. More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand. Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline. However, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets. In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length. We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT. AU - Wever, Marcel Dominik AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 3852 KW - automated machine learning KW - complex pipelines KW - hierarchical planning T2 - ICML 2018 AutoML Workshop TI - ML-Plan for Unlimited-Length Machine Learning Pipelines ER - TY - CONF AB - In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance. AU - Wever, Marcel Dominik AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 2109 KW - Classification KW - Hierarchical Decomposition KW - Indirect Encoding T2 - Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018 TI - Ensembles of Evolved Nested Dichotomies for Classification ER - TY - GEN AU - Wever, Marcel Dominik AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 17713 TI - Automated Multi-Label Classification based on ML-Plan ER - TY - GEN AU - Mohr, Felix AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ID - 17714 TI - Automated machine learning service composition ER - TY - GEN AU - Graf, Helena ID - 5693 TI - Ranking of Classification Algorithms in AutoML ER - TY - GEN AU - Scheibl, Manuel ID - 5936 TI - Learning about learning curves from dataset properties ER - TY - CHAP AU - Schäfer, Dirk AU - Hüllermeier, Eyke ID - 6423 SN - 0302-9743 T2 - Discovery Science TI - Preference-Based Reinforcement Learning Using Dyad Ranking ER - TY - GEN ED - Abiteboul, S. ED - Arenas, M. ED - Barceló, P. ED - Bienvenu, M. ED - Calvanese, D. ED - David, C. ED - Hull, R. ED - Hüllermeier, Eyke ED - Kimelfeld, B. ED - Libkin, L. ED - Martens, W. ED - Milo, T. ED - Murlak, F. ED - Neven, F. ED - Ortiz, M. ED - Schwentick, T. ED - Stoyanovich, J. ED - Su, J. ED - Suciu, D. ED - Vianu, V. ED - Yi, K. ID - 10591 IS - 1 TI - Research Directions for Principles of Data Management VL - 7 ER - TY - CHAP AU - Couso, Ines AU - Hüllermeier, Eyke ED - Mostaghim, Sanaz ED - Nürnberger, Andreas ED - Borgelt, Christian ID - 10783 T2 - Frontiers in Computational Intelligence TI - Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators ER - TY - JOUR AU - Schäfer, D. AU - Hüllermeier, Eyke ID - 16038 IS - 5 JF - Machine Learning TI - Dyad ranking using Plackett-Luce models based on joint feature representations VL - 107 ER - TY - CONF AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke ID - 10145 T2 - Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) TI - Learning to Rank Based on Analogical Reasoning ER - 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 - TY - CONF AU - Melnikov, Vitaly AU - Hüllermeier, Eyke ED - Hoffmann, F. ED - Hüllermeier, Eyke ED - Mikut, R. ID - 15397 T2 - in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany TI - Optimizing the structure of nested dichotomies. A comparison of two heuristics ER - TY - CONF AU - Czech, M. AU - Hüllermeier, Eyke AU - Jacobs, M.C. AU - Wehrheim, Heike ID - 15399 T2 - in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany TI - Predicting rankings of software verification tools ER - TY - CONF AU - Couso, Ines AU - Dubois, D. AU - Hüllermeier, Eyke ID - 15110 T2 - in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain TI - Maximum likelihood estimation and coarse data ER - TY - CONF AU - Ewerth, Ralph AU - Springstein, M. AU - Müller, E. AU - Balz, A. AU - Gehlhaar, J. AU - Naziyok, T. AU - Dembczynski, K. AU - Hüllermeier, Eyke ID - 10204 T2 - Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) TI - Estimating relative depth in single images via rankboost ER - TY - CONF AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke AU - Couso, Ines ID - 10205 T2 - Proc. 34th Int. Conf. on Machine Learning (ICML 2017) TI - Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening ER - TY - CONF AU - Mohr, Felix AU - Lettmann, Theodor AU - Hüllermeier, Eyke ID - 10206 T2 - Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) TI - Planning with Independent Task Networks ER - TY - CONF AU - Czech, M. AU - Hüllermeier, Eyke AU - Jakobs, M.-C. AU - Wehrheim, Heike ID - 10207 T2 - Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 TI - Predicting rankings of software verification tools ER - TY - CONF AU - Couso, Ines AU - Dubois, D. AU - Hüllermeier, Eyke ID - 10208 T2 - Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) TI - Maximum Likelihood Estimation and Coarse Data ER - TY - CONF AU - Ahmadi Fahandar, Mohsen AU - Hüllermeier, Eyke ID - 10209 T2 - Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence TI - Learning to Rank based on Analogical Reasoning ER - TY - CONF AU - Hoffmann, F. AU - Hüllermeier, Eyke AU - Mikut, R. ID - 10212 TI - (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017 ER -