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

Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label Classification. 2019.
LibreCat: /record/8868

Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019, pp. 191–213.
LibreCat: /record/10578

Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.”10.1109/mci.2018.2881642__IEEE Computational Intelligence Magazine, 2019, pp. 31–44, doi:10.1109/mci.2018.2881642.
LibreCat: /record/15001
DOI: 10.1109/mci.2018.2881642

Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems and Methods.”10.1007/s10618-018-0595-5__Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019, pp. 293–324, doi:10.1007/s10618-018-0595-5.
LibreCat: /record/15002
DOI: 10.1007/s10618-018-0595-5

Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.” 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.”10.1007/978-3-030-33778-0_22__Discovery Science, 2019, doi: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.”10.1007/978-3-030-30179-8_3__KI 2019: Advances in Artificial Intelligence, 2019, doi:10.1007/978-3-030-30179-8_3.
LibreCat: /record/15005
DOI: 10.1007/978-3-030-30179-8_3

Nguyen, Vu-Linh, et al. “Epistemic Uncertainty Sampling.”10.1007/978-3-030-33778-0_7__Discovery Science, 2019, doi: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.”10.1016/j.jmva.2019.02.017__Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019, doi:10.1016/j.jmva.2019.02.017.
LibreCat: /record/15007
DOI: 10.1016/j.jmva.2019.02.017

Epple, Nico, et al. “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries.”10.1109/ivs.2019.8814100__2019 IEEE Intelligent Vehicles Symposium (IV), 2019, doi:10.1109/ivs.2019.8814100.
LibreCat: /record/15009
DOI: 10.1109/ivs.2019.8814100

Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–46.
LibreCat: /record/15011

Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass Classification.” Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2019.
LibreCat: /record/15013

Hüllermeier, Eyke, et al. “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.
LibreCat: /record/15014

Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.”10.1145/3363572__ACM Transactions on Knowledge Discovery from Data, 2019, pp. 1–36, doi:10.1145/3363572.
LibreCat: /record/15015
DOI: 10.1145/3363572

Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.”10.1016/j.jmva.2019.02.017__Journal of Multivariate Analysis, 2019, pp. 291–312, doi: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.”10.1214/19-ejs1555__Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555.
LibreCat: /record/14028
DOI: 10.1214/19-ejs1555

Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., 2019, pp. 273–74.
LibreCat: /record/13132

Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019.
LibreCat: /record/10232

Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.”10.1109/TCDS.2019.2892991__IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991.
LibreCat: /record/20243
DOI: 10.1109/TCDS.2019.2892991

Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.”10.1109/SCC.2018.00039__SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039.
LibreCat: /record/2479
DOI: 10.1109/SCC.2018.00039

Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.
LibreCat: /record/19524

Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.
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, vol. 64, no. 6, IEEE, 2018, pp. 2614–20.
LibreCat: /record/24150

Demirel, Burak, et al. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” IEEE Control Systems Letters, vol. 2, no. 4, IEEE, 2018, pp. 737–42.
LibreCat: /record/24151

Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.”10.1109/SCC.2018.00036__SCC, IEEE Computer Society, 2018, doi: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.”10.1007/s10994-018-5733-1__Machine Learning, 2018, doi:10.1007/s10994-018-5733-1.
LibreCat: /record/3402
DOI: 10.1007/s10994-018-5733-1

Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.”10.1007/s10994-018-5735-z__Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.
LibreCat: /record/3510
DOI: 10.1007/s10994-018-5735-z

Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.”10.1007/978-3-030-01768-2_19__Proceedings of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19.
LibreCat: /record/3552
DOI: 10.1007/978-3-030-01768-2_19

Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” ICML 2018 AutoML Workshop, 2018.
LibreCat: /record/3852

Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classification.”10.1145/3205455.3205562__Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 2018, doi:10.1145/3205455.3205562.
LibreCat: /record/2109
DOI: 10.1145/3205455.3205562

Wever, Marcel Dominik, et al. Automated Multi-Label Classification Based on ML-Plan. Arxiv, 2018.
LibreCat: /record/17713

Mohr, Felix, et al. 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.”10.1007/978-3-030-01771-2_11__Discovery Science, Springer International Publishing, 2018, pp. 161–75, doi:10.1007/978-3-030-01771-2_11.
LibreCat: /record/6423
DOI: 10.1007/978-3-030-01771-2_11

Abiteboul, S., et al., editors. Research Directions for Principles of Data Management. Vol. 7, no. 1, 2018, pp. 1–29.
LibreCat: /record/10591

Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” Frontiers in Computational Intelligence, edited by Sanaz Mostaghim et al., Springer, 2018, pp. 31–46.
LibreCat: /record/10783

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

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–58.
LibreCat: /record/10145

El Mesaoudi-Paul, Adil, et al. “Ranking Distributions Based on Noisy Sorting.” Proc. 35th Int. Conference on Machine Learning (ICML), Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2018, pp. 3469–77.
LibreCat: /record/10148

Hesse, M., et al. “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.” Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 2018, pp. 15–20.
LibreCat: /record/10149

Mencia, E. Loz., et al. “Learning Interpretable Rules for Multi-Label Classification.” Explainable and Interpretable Models in Computer Vision and Machine Learning, edited by H. Jair Escalante et al., Springer, 2018, pp. 81–113.
LibreCat: /record/10152

Nguyen, Vu-Linh, et al. “Reliable Multi-Class Classification Based on Pairwise Epistemic and Aleatoric Uncertainty.” Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–95.
LibreCat: /record/10181

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

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

Seemann, Nina, et al. “Supporting the Cognitive Process in Annotation Tasks.” 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, vol. 63, no. 5, IEEE, 2017, pp. 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, vol. 42, no. 3, INFORMS, 2017, pp. 648–61.
LibreCat: /record/24153

Melnikov, Vitalik, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics.”10.5445/KSP/1000074341__Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, KIT Scientific Publishing, 2017, doi:10.5445/KSP/1000074341.
LibreCat: /record/3325
DOI: 10.5445/KSP/1000074341

Jakobs, Marie-Christine, et al. “Certiû01cation Matters for Service Markets.” The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus Uhl, 2017, pp. 7–12.
LibreCat: /record/115

Seemann, Nina, et al. “Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German.”10.18653/v1/W17-2206__Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Association for Computational Linguistics (ACL), 2017, pp. 40–45, doi: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, et al. 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, et al. “Predicting Rankings of Software Verification Tools.”10.1145/3121257.3121262__Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26, doi:10.1145/3121257.3121262.
LibreCat: /record/71
DOI: 10.1145/3121257.3121262

Czech, Mike, et al. Predicting Rankings of Software Verification Competitions. 2017.
LibreCat: /record/72

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–05.
LibreCat: /record/10589

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, vol. 107, Springer, 2017, pp. 1000–05.
LibreCat: /record/10784

Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence, 2017.
LibreCat: /record/1180

Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies. A Comparison of Two Heuristics.” In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann et al., KIT Scientific Publishing, 2017, pp. 1–12.
LibreCat: /record/15397

Czech, M., et al. “Predicting Rankings of Software Verification Tools.” In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017.
LibreCat: /record/15399

Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017, pp. 3–16.
LibreCat: /record/15110

Ewerth, Ralph, et al. “Estimating Relative Depth in Single Images via Rankboost.” Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–24.
LibreCat: /record/10204

Ahmadi Fahandar, Mohsen, et al. “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening.” Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–87.
LibreCat: /record/10205

Mohr, Felix, et al. “Planning with Independent Task Networks.”10.1007/978-3-319-67190-1_15__Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206, doi:10.1007/978-3-319-67190-1_15.
LibreCat: /record/10206
DOI: 10.1007/978-3-319-67190-1_15

Czech, M., et al. “Predicting Rankings of Software Verification Tools.” Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.
LibreCat: /record/10207

Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16.
LibreCat: /record/10208

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

Hoffmann, F., et al. (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. 2017.
LibreCat: /record/10212

Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics.” Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017, 2017, pp. 1–12.
LibreCat: /record/10213

Shaker, Ammar, et al. “Learning TSK Fuzzy Rules from Data Streams.” Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia, 2017.
LibreCat: /record/10216

Bräuning, M., et al. “Lexicographic Preferences for Predictive Modeling of Human Decision Making. A New Machine Learning Method with an Application  in Accounting.” European Journal of Operational Research, vol. 258, no. 1, 2017, pp. 295–306.
LibreCat: /record/10267

Platenius, M. C., et al. “Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic.” IEEE Transactions on Software Engineering, vol. 43, no. 8, 2017, pp. 739–59.
LibreCat: /record/10268

Hüllermeier, Eyke. “From Knowledge-Based to Data-Driven Modeling of Fuzzy Rule-Based Systems: A Critical Reflection.” The Computing Research Repository  (CoRR), abs/1712.00646, 2017.
LibreCat: /record/10269

Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stochastic Recursive Inclusion in Two Timescales with an Application to the Lagrangian Dual Problem.” Stochastics, vol. 88, no. 8, Taylor \'26; Francis, 2016, pp. 1173–87.
LibreCat: /record/24154

Melnikov, Vitalik, et al. “Pairwise versus Pointwise Ranking: A Case Study.”10.4467/20838476si.16.006.6187__Schedae Informaticae, vol. 25, Uniwersytet Jagiellonski - Wydawnictwo Uniwersytetu Jagiellonskiego, 2016, doi:10.4467/20838476si.16.006.6187.
LibreCat: /record/3318
DOI: 10.4467/20838476si.16.006.6187

Platenius, Marie Christin, et al. “Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic.”10.1109/TSE.2016.2632115__IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, no. 8, IEEE, 2016, pp. 739–59, doi:10.1109/TSE.2016.2632115.
LibreCat: /record/190
DOI: 10.1109/TSE.2016.2632115

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.”10.1007/978-3-319-46227-1_47__Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–71, doi:10.1007/978-3-319-46227-1_47.
LibreCat: /record/184
DOI: 10.1007/978-3-319-46227-1_47

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, Springer, 2016.
LibreCat: /record/10785

Labreuche, C., et al. “On the Identifiability of Models  in Multi-Criteria Preference Learning.” In Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, edited by R. Busa-Fekete et al., 2016.
LibreCat: /record/15400

Schäfer, D., and Eyke Hüllermeier. “Preference -Based Reinforcement Learning Using Dyad Ranking.” In Proceedings DPL16 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany, edited by R. Busa-Fekete et al., 2016.
LibreCat: /record/15401

Couso, Ines, et al. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” In Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, edited by R. Busa-Fekete et al., 2016.
LibreCat: /record/15402

Lu, S., and Eyke Hüllermeier. “Support Vector Classification on Noisy Data Using Fuzzy Superset Losses.” In Proceedings 26th Workshop Computational Intelligence, Dortmund Germany, edited by Eyke Hüllermeier et al., KIT Scientific Publishing, 2016, pp. 1–8.
LibreCat: /record/15403

Schäfer, D., and Eyke Hüllermeier. “Plackett-Luce Networks for Dyad Ranking.” In Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany, 2016.
LibreCat: /record/15404

Pfannschmidt, Karlson, et al. “Evaluating Tests in Medical  Diagnosis-Combining Machine Learning with Game-Theoretical Concepts.” 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. 450–61.
LibreCat: /record/15111

Leinweber, M., et al. “CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites.” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, 2016, pp. 1423–34.
LibreCat: /record/16041

Mohr, Felix. Towards Automated Service Composition Under Quality Constraints. Universität Paderborn, 2016, doi:10.17619/UNIPB/1-171.
LibreCat: /record/141
DOI: 10.17619/UNIPB/1-171

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, Springer, 2016.
LibreCat: /record/10214

Hoffmann, F., et al., editors.  Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany. 2016.
LibreCat: /record/10221

Jasinska, K., et al. “Extreme F-Measure Maximization Using Sparse Probability Estimates .” Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, edited by M.F. Balcan and K.Q. Weinberger, 2016.
LibreCat: /record/10222

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms,  in Proceedings ECML/PKDD-2016.” European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 756–71.
LibreCat: /record/10223

Dembczynski, K., et al. “Consistency of Probalistic Classifier Trees.” In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 511–26.
LibreCat: /record/10224

Shabani, Aulon, et al. “Predicting the Electricity Consumption of Buildings: An Improved CBR Approach.” In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 2016, pp. 356–69.
LibreCat: /record/10225

Pfannschmidt, Karlson, et al. “Evaluating Tests in Medical  Diagnosis-Combining Machine Learning with Game-Theoretical Concepts.” 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. 450–61.
LibreCat: /record/10226

Labreuche, C., et al. “On the Identifiability of Models in Multi-Criteria Preference Learning .” Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete et al., 2016.
LibreCat: /record/10227

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete et al., 2016.
LibreCat: /record/10228

Couso, Ines, et al. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete et al., 2016.
LibreCat: /record/10229

Lu, S., and Eyke Hüllermeier. “Support Vector Classification on Noisy Data Using Fuzzy Supersets Losses.” Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, edited by F. Hoffmann et al., 2016, pp. 1–8.
LibreCat: /record/10230

Schäfer, Dirk, and Eyke Hüllermeier. “Plackett-Luce Networks for Dyad Ranking.” In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.
LibreCat: /record/10231