@inproceedings{2479, author = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke and Faez, Amin}}, booktitle = {{SCC}}, location = {{San Francisco, CA, USA}}, publisher = {{IEEE}}, title = {{{(WIP) Towards the Automated Composition of Machine Learning Services}}}, doi = {{10.1109/SCC.2018.00039}}, year = {{2018}}, } @unpublished{19524, abstract = {{Object ranking is an important problem in the realm of preference learning.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.}}, author = {{Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}}, booktitle = {{arXiv:1803.05796}}, title = {{{Deep Architectures for Learning Context-dependent Ranking Functions}}}, year = {{2018}}, } @inproceedings{2857, author = {{Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke and Wever, Marcel Dominik}}, booktitle = {{Proceedings of the 1st ICAPS Workshop on Hierarchical Planning}}, location = {{Delft, Netherlands}}, pages = {{31--39}}, publisher = {{AAAI}}, title = {{{Programmatic Task Network Planning}}}, year = {{2018}}, } @article{24150, author = {{Ramaswamy, Arunselvan and Bhatnagar, Shalabh}}, journal = {{IEEE Transactions on Automatic Control}}, number = {{6}}, pages = {{2614--2620}}, publisher = {{IEEE}}, title = {{{Stability of stochastic approximations with “controlled markov” noise and temporal difference learning}}}, volume = {{64}}, year = {{2018}}, } @article{24151, author = {{Demirel, Burak and Ramaswamy, Arunselvan and Quevedo, Daniel E and Karl, Holger}}, journal = {{IEEE Control Systems Letters}}, number = {{4}}, pages = {{737--742}}, publisher = {{IEEE}}, title = {{{Deepcas: A deep reinforcement learning algorithm for control-aware scheduling}}}, volume = {{2}}, year = {{2018}}, } @inproceedings{2471, author = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{SCC}}, location = {{San Francisco, CA, USA}}, publisher = {{IEEE Computer Society}}, title = {{{On-The-Fly Service Construction with Prototypes}}}, doi = {{10.1109/SCC.2018.00036}}, year = {{2018}}, } @article{3402, abstract = {{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.}}, author = {{Melnikov, Vitalik and Hüllermeier, Eyke}}, issn = {{1573-0565}}, journal = {{Machine Learning}}, title = {{{On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}}}, doi = {{10.1007/s10994-018-5733-1}}, year = {{2018}}, } @article{3510, abstract = {{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.}}, author = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}}, issn = {{1573-0565}}, journal = {{Machine Learning}}, keywords = {{AutoML, Hierarchical Planning, HTN planning, ML-Plan}}, location = {{Dublin, Ireland}}, pages = {{1495--1515}}, publisher = {{Springer}}, title = {{{ML-Plan: Automated Machine Learning via Hierarchical Planning}}}, doi = {{10.1007/s10994-018-5735-z}}, year = {{2018}}, } @inproceedings{3552, author = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the Symposium on Intelligent Data Analysis}}, location = {{‘s-Hertogenbosch, the Netherlands}}, title = {{{Reduction Stumps for Multi-Class Classification}}}, doi = {{10.1007/978-3-030-01768-2_19}}, year = {{2018}}, } @inproceedings{3852, abstract = {{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.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{ICML 2018 AutoML Workshop}}, keywords = {{automated machine learning, complex pipelines, hierarchical planning}}, location = {{Stockholm, Sweden}}, title = {{{ML-Plan for Unlimited-Length Machine Learning Pipelines}}}, year = {{2018}}, } @inproceedings{2109, abstract = {{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.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}}, keywords = {{Classification, Hierarchical Decomposition, Indirect Encoding}}, location = {{Kyoto, Japan}}, publisher = {{ACM}}, title = {{{Ensembles of Evolved Nested Dichotomies for Classification}}}, doi = {{10.1145/3205455.3205562}}, year = {{2018}}, } @unpublished{17713, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, publisher = {{Arxiv}}, title = {{{Automated Multi-Label Classification based on ML-Plan}}}, year = {{2018}}, } @unpublished{17714, author = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}}, title = {{{Automated machine learning service composition}}}, year = {{2018}}, } @misc{5693, author = {{Graf, Helena}}, publisher = {{Universität Paderborn}}, title = {{{Ranking of Classification Algorithms in AutoML}}}, year = {{2018}}, } @misc{5936, author = {{Scheibl, Manuel}}, publisher = {{Universität Paderborn}}, title = {{{Learning about learning curves from dataset properties}}}, year = {{2018}}, } @inbook{6423, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, isbn = {{9783030017705}}, issn = {{0302-9743}}, pages = {{161--175}}, publisher = {{Springer International Publishing}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, doi = {{10.1007/978-3-030-01771-2_11}}, year = {{2018}}, } @proceedings{10591, editor = {{Abiteboul, S. and Arenas, M. and Barceló, P. and Bienvenu, M. and Calvanese, D. and David, C. and Hull, R. and Hüllermeier, Eyke and Kimelfeld, B. and Libkin, L. and Martens, W. and Milo, T. and Murlak, F. and Neven, F. and Ortiz, M. and Schwentick, T. and Stoyanovich, J. and Su, J. and Suciu, D. and Vianu, V. and Yi, K.}}, number = {{1}}, pages = {{1--29}}, title = {{{Research Directions for Principles of Data Management}}}, volume = {{7}}, year = {{2018}}, } @inbook{10783, author = {{Couso, Ines and Hüllermeier, Eyke}}, booktitle = {{Frontiers in Computational Intelligence}}, editor = {{Mostaghim, Sanaz and Nürnberger, Andreas and Borgelt, Christian}}, pages = {{31--46}}, publisher = {{Springer}}, title = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}}, year = {{2018}}, } @article{16038, author = {{Schäfer, D. and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{5}}, pages = {{903--941}}, title = {{{Dyad ranking using Plackett-Luce models based on joint feature representations}}}, volume = {{107}}, year = {{2018}}, } @inproceedings{10145, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI)}}, pages = {{2951--2958}}, title = {{{Learning to Rank Based on Analogical Reasoning}}}, year = {{2018}}, } @inproceedings{10148, author = {{El Mesaoudi-Paul, Adil and Hüllermeier, Eyke and Busa-Fekete, Robert}}, booktitle = {{Proc. 35th Int. Conference on Machine Learning (ICML)}}, pages = {{3469--3477}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Ranking Distributions based on Noisy Sorting}}}, year = {{2018}}, } @inproceedings{10149, author = {{Hesse, M. and Timmermann, J. and Hüllermeier, Eyke and Trächtler, Ansgar}}, booktitle = {{Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24}}, pages = {{15--20}}, title = {{{A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart}}}, year = {{2018}}, } @inbook{10152, author = {{Mencia, E.Loza and Fürnkranz, J. and Hüllermeier, Eyke and Rapp, M.}}, booktitle = {{Explainable and Interpretable Models in Computer Vision and Machine Learning}}, editor = {{Jair Escalante, H. and Escalera, S. and Guyon, I. and Baro, X. and Güclüütürk, Y. and Güclü, U. and van Gerven, M.A.J.}}, pages = {{81--113}}, publisher = {{Springer}}, title = {{{Learning interpretable rules for multi-label classification}}}, year = {{2018}}, } @inproceedings{10181, author = {{Nguyen, Vu-Linh and Destercke, Sebastian and Masson, M.-H. and Hüllermeier, Eyke}}, booktitle = {{Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI)}}, pages = {{5089--5095}}, title = {{{Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty}}}, year = {{2018}}, } @inproceedings{10184, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Proc. 21st Int. Conference on Discovery Science (DS)}}, pages = {{161--175}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, year = {{2018}}, } @article{10276, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{5}}, pages = {{903--941}}, title = {{{Dyad Ranking Using Plackett-Luce Models based on joint feature representations}}}, volume = {{107}}, year = {{2018}}, } @inproceedings{1379, author = {{Seemann, Nina and Geierhos, Michaela and Merten, Marie-Luis and Tophinke, Doris and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft}}, editor = {{Eckart, Kerstin and Schlechtweg, Dominik }}, location = {{Stuttgart, Germany}}, title = {{{Supporting the Cognitive Process in Annotation Tasks}}}, year = {{2018}}, } @article{24152, author = {{Ramaswamy, Arunselvan and Bhatnagar, Shalabh}}, journal = {{IEEE Transactions on Automatic Control}}, number = {{5}}, pages = {{1465--1471}}, publisher = {{IEEE}}, title = {{{Analysis of gradient descent methods with nondiminishing bounded errors}}}, volume = {{63}}, year = {{2017}}, } @article{24153, author = {{Ramaswamy, Arunselvan and Bhatnagar, Shalabh}}, journal = {{Mathematics of Operations Research}}, number = {{3}}, pages = {{648--661}}, publisher = {{INFORMS}}, title = {{{A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions}}}, volume = {{42}}, year = {{2017}}, } @inproceedings{3325, author = {{Melnikov, Vitalik and Hüllermeier, Eyke}}, booktitle = {{Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017}}, publisher = {{KIT Scientific Publishing}}, title = {{{Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics}}}, doi = {{10.5445/KSP/1000074341}}, year = {{2017}}, } @inproceedings{115, abstract = {{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.}}, author = {{Jakobs, Marie-Christine and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}}, booktitle = {{The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}}, editor = {{Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz}}, pages = {{7--12}}, title = {{{Certification Matters for Service Markets}}}, year = {{2017}}, } @inproceedings{1158, abstract = {{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.}}, author = {{Seemann, Nina and Merten, Marie-Luis and Geierhos, Michaela and Tophinke, Doris and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature}}, location = {{Vancouver, BC, Canada}}, pages = {{40--45}}, publisher = {{Association for Computational Linguistics (ACL)}}, title = {{{Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German}}}, doi = {{10.18653/v1/W17-2206}}, year = {{2017}}, } @misc{5694, author = {{Schnitker, Nino Noel}}, publisher = {{Universität Paderborn}}, title = {{{Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies}}}, year = {{2017}}, } @inproceedings{5722, author = {{Gupta, Pritha and Hetzer, Alexander and Tornede, Tanja and Gottschalk, Sebastian and Kornelsen, Andreas and Osterbrink, Sebastian and Pfannschmidt, Karlson and Hüllermeier, Eyke}}, location = {{Rostock}}, title = {{{jPL: A Java-based Software Framework for Preference Learning}}}, year = {{2017}}, } @misc{5724, author = {{Hetzer, Alexander and Tornede, Tanja}}, publisher = {{Universität Paderborn}}, title = {{{Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction}}}, year = {{2017}}, } @inproceedings{71, abstract = {{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).}}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, booktitle = {{Proceedings of the 3rd International Workshop on Software Analytics}}, pages = {{23--26}}, title = {{{Predicting Rankings of Software Verification Tools}}}, doi = {{10.1145/3121257.3121262}}, year = {{2017}}, } @techreport{72, abstract = {{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). }}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, title = {{{Predicting Rankings of Software Verification Competitions}}}, year = {{2017}}, } @misc{10589, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, pages = {{1000--1005}}, title = {{{Preference Learning}}}, year = {{2017}}, } @inbook{10784, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, editor = {{Sammut, C. and Webb, G.I.}}, pages = {{1000--1005}}, publisher = {{Springer}}, title = {{{Preference Learning}}}, volume = {{107}}, year = {{2017}}, } @inproceedings{1180, abstract = {{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.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @inproceedings{15397, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, pages = {{1--12}}, publisher = {{KIT Scientific Publishing}}, title = {{{Optimizing the structure of nested dichotomies. A comparison of two heuristics}}}, year = {{2017}}, } @inproceedings{15399, author = {{Czech, M. and Hüllermeier, Eyke and Jacobs, M.C. and Wehrheim, Heike}}, booktitle = {{in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany}}, title = {{{Predicting rankings of software verification tools}}}, year = {{2017}}, } @inproceedings{15110, author = {{Couso, Ines and Dubois, D. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain}}, pages = {{3--16}}, publisher = {{Springer}}, title = {{{Maximum likelihood estimation and coarse data}}}, year = {{2017}}, } @inproceedings{10204, author = {{Ewerth, Ralph and Springstein, M. and Müller, E. and Balz, A. and Gehlhaar, J. and Naziyok, T. and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017)}}, pages = {{919--924}}, title = {{{Estimating relative depth in single images via rankboost}}}, year = {{2017}}, } @inproceedings{10205, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke and Couso, Ines}}, booktitle = {{Proc. 34th Int. Conf. on Machine Learning (ICML 2017)}}, pages = {{1078--1087}}, title = {{{Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening}}}, year = {{2017}}, } @inproceedings{10206, author = {{Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke}}, booktitle = {{Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017)}}, pages = {{193--206}}, title = {{{Planning with Independent Task Networks}}}, doi = {{10.1007/978-3-319-67190-1_15}}, year = {{2017}}, } @inproceedings{10207, author = {{Czech, M. and Hüllermeier, Eyke and Jakobs, M.-C. and Wehrheim, Heike}}, booktitle = {{Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017}}, pages = {{23--26}}, title = {{{Predicting rankings of software verification tools}}}, year = {{2017}}, } @inproceedings{10208, author = {{Couso, Ines and Dubois, D. and Hüllermeier, Eyke}}, booktitle = {{Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017)}}, pages = {{3--16}}, title = {{{Maximum Likelihood Estimation and Coarse Data}}}, year = {{2017}}, } @inproceedings{10209, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence}}, title = {{{Learning to Rank based on Analogical Reasoning}}}, year = {{2017}}, } @inproceedings{10212, author = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, title = {{{(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017}}}, year = {{2017}}, }