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Anzahl der Publikationen auf dieser Ebene: 393

Herausgeberschaft

Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke

und Meo, Rosa (Hrsg.) (2014): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I. Lecture Notes in Computer Science, Bd. 8724. Berlin, Heidelberg: Springer.

Fürnkranz, Johannes und Hüllermeier, Eyke

(Hrsg.) (2010): Preference Learning. Berlin: Springer.

Hoffmann, Frank und Hüllermeier, Eyke

(Hrsg.) (2015): Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Bd. 54. Karlsruhe: KIT Scientific Publishing. [PDF, 18MB]

Hoffmann, Frank und Hüllermeier, Eyke

(Hrsg.) (2013): Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5. - 6. Dezember 2013. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Bd. 46. Karlsruhe: KIT Scientific Publishing. [PDF, 18MB]

Hoffmann, Frank und Hüllermeier, Eyke

(Hrsg.) (2009): Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH), Bd. 29. Karlsruhe: KIT Scientific Publishing. [PDF, 19MB]

Hoffmann, Frank; Hüllermeier, Eyke

und Mikut, Ralf (Hrsg.) (2016): Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016. Karlsruhe: KIT Scientific Publishing. [PDF, 14MB]

Hüllermeier, Eyke

und Minor, Mirjam (Hrsg.) (2015): Case-Based Reasoning Research and Development. 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany, September 28-30, 2015. Proceedings. Lecture Notes in Computer Science, Bd. 9343. Cham: Springer.

Mikut, Ralf; Hüllermeier, Eyke

und Hoffmann, Frank (Hrsg.) (2017): Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. Karlsruhe: KIT Scientific Publishing. [PDF, 11MB]

Fürnkranz, Johannes; Hüllermeier, Eyke

; Rudin, Cynthia; Slowinski, Roman und Sanner, Scott (Hrsg.) (2014): Preference Learning. Report from Dagstuhl Seminar 14101. Dagstuhl Reports, Bd. 4. Dagstuhl: Schloss Dagstuhl, Leibniz-Zentrum fuer Informatik. [PDF, 1MB]

Hochschulschrift

Bengs, Viktor (2018): Confidence sets for change-point problems in nonparametric regression. Dissertation, Philipps-Universität Marburg

Wever, Marcel

(2021): Automated Machine Learning for Multi-Label Classification. Dissertation, Universität Paderborn
[PDF, 8MB]

Wever, Marcel Dominik

(März 2017): Active Learning of User Requirements Specifications in Dynamic Software Service Markets. Masterarbeit, Universität Paderborn

Zeitschriftenartikel

Abbaszadeh, Sadegh

und Hüllermeier, Eyke

(Dezember 2021): Machine Learning With the Sugeno Integral: The Case of Binary Classification. In: IEEE Transactions on Fuzzy Systems, Nr. 12: S. 3723-3733

Abiteboul, Serge; Arenas, Marcelo; Barceló, Pablo; Bienvenu, Meghyn; Calvanese, Diego; David, Claire; Hull, Richard; Hüllermeier, Eyke

; Kimelfeld, Benny; Libkin, Leonid; Martens, Wim; Milo, Tova; Murlak, Filip; Neven, Frank; Ortiz, Magdalena; Schwentick, Thomas; Stoyanovich, Julia; Su, Jianwen; Suciu, Dan; Vianu, Victor und Yi, Ke (Dezember 2016): Research Directions for Principles of Data Management (Abridged). In: SIGMOD Record, Bd. 45, Nr. 4: 5–17

Abiteboul, Serge; Arenas, Marcelo; Barceló, Pablo; Bienvenu, Meghyn; Calvanese, Diego; David, Claire; Hull, Richard; Hüllermeier, Eyke

; Kimelfeld, Benny; Libkin, Leonid; Martens, Wim; Milo, Tova; Murlak, Filip; Neven, Frank; Ortiz, Magdalena; Schwentick, Thomas; Stoyanovich, Julia; Su, Jianwen; Suciu, Dan; Vianu, Victor und Yi, Ke (April 2018): Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). In: Dagstuhl Manifestos, Bd. 7, Nr. 1: S. 1-29 [PDF, 519kB]

Agarwal, Manish; Fallah Tehrani, Ali und Hüllermeier, Eyke

(2014): Preference-based Learning of Ideal Solutions in TOPSIS-like Decision Models. In: Journal of Multi-Criteria Decision Analysis, Bd. 22, Nr. 3-4: S. 175-183

Bengs, Viktor; Busa-Fekete, Róbert; Mesaoudi-Paul, Adil El und Hüllermeier, Eyke

(Januar 2021): Preference-based Online Learning with Dueling Bandits: A Survey. In: Journal of Machine Learning Research, Bd. 22, Nr. 7: S. 1-108 [PDF, 918kB]

Bengs, Viktor

und Hüllermeier, Eyke

(16. November 2022): Multi-armed bandits with censored consumption of resources. In: Machine Learning, Bd. 112, Nr. 2: S. 217-240 [PDF, 2MB]

Bengs, Viktor; Eulert, Matthias und Holzmann, Hajo (2019): Asymptotic confidence sets for the jump curve in bivariate regression problems. In: Journal of Multivariate Analysis, Bd. 173: S. 291-312

Bengs, Viktor und Holzmann, Hajo (2019): Adaptive confidence sets for kink estimation. In: Electronic Journal of Statistics, Bd. 13, Nr. 1: S. 1523-1579 [PDF, 726kB]

Bode, Henrik; Heid, Stefan; Weber, Daniel; Hüllermeier, Eyke

und Wallscheid, Oliver

(Mai 2020): Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. In: Journal of Open Source Software, Bd. 5, Nr. 54: S. 2435 [PDF, 388kB]

Bodenhofer, Ulrich; Hüllermeier, Eyke

; Klawonn, Frank und Kruse, Rudolf (2006): Special Issue on Soft Computing for Information Mining. Editorial. In: Soft Computing, Bd. 11, Nr. 5: S. 397-399

Bräuning, Michael und Hüllermeier, Eyke

(Mai 2016): Learning Conditional Lexicographic Preference Tree. In: Archives of data science. Series A, Bd. 1, Nr. 1: S. 41-55 [PDF, 202kB]

Bräuning, Michael; Hüllermeier, Eyke

; Keller, Tobias und Glaum, Martin (2017): Lexicographic preferences for predictive modeling of human decision making: A new machine learning method with an application in accounting. In: European Journal of Operational Research, Bd. 258, Nr. 1: S. 295-306

Busa-Fekete, Róbert; Szörényi, Balázs; Weng, Paul; Cheng, Weiwei und Hüllermeier, Eyke

(2014): Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. In: Machine Learning, Bd. 97, Nr. 3: S. 327-351

Bustince, Humberto; Pagola, Miguel; Mesiar, Radko; Hüllermeier, Eyke

und Herrera, Francisco (2012): Grouping, Overlap, and Generalized Bientropic Functions for Fuzzy Modeling of Pairwise Comparisons. In: IEEE Transactions on Fuzzy Systems, Bd. 20, Nr. 3: S. 405-415

Bösner, Stefan; Bönisch, Katharina; Haasenritter, Jörg; Schlegel, Patrice; Hüllermeier, Eyke

und Donner-Banzhoff, Norbert (2013): Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? - A cross sectional study. In: BMC Family Practice, Bd. 14, 154 [PDF, 1MB]

Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke

und Meo, Rosa (2014): Guest Editors’ introduction: special issue of the ECML/PKDD 2014 journal track. In: Machine Learning, Bd. 97, Nr. 1-2: S. 1-3

Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke

und Meo, Rosa (2014): Guest editors’ introduction: special issue of the ECML/PKDD 2014 journal track. In: Data Mining and Knowledge Discovery, Bd. 28, Nr. 5-6: S. 1129-1133

Camberg, Simone

und Hüllermeier, Eyke

(November 2024): An Extensive Analysis of Different Approaches to Driver Gaze Classification. In: IEEE Transactions on Intelligent Transportation Systems, Bd. 25, Nr. 11: S. 16435-16448

Campagner, Andrea

; Ciucci, Davide

und Hüllermeier, Eyke

(Juni 2021): Rough set-based feature selection for weakly labeled data. In: International Journal of Approximate Reasoning: S. 150-167

Cheng, Weiwei und Hüllermeier, Eyke

(2009): Combining instance-based learning and logistic regression for multilabel classification. In: Machine Learning, Bd. 76, Nr. 2-3: S. 211-225

Couso, Ines; Borgelt, Christian; Hüllermeier, Eyke

und Kruse, Rudolf (2019): Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. In: IEEE Computational Intelligence Magazine, Bd. 14, Nr. 1: S. 31-44

Dellnitz, Michael; Hüllermeier, Eyke

; Lücke, Marvin

; Ober-Blöbaum, Sina; Offen, Christian

; Peitz, Sebastian

und Pfannschmidt, Karlson

(2023): Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning. In: SIAM Journal on Scientific Computing, Bd. 45, Nr. 2: A579-A595 [PDF, 671kB]

Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei und Hüllermeier, Eyke

(2012): On label dependence and loss minimization in multi-label classification. In: Machine Learning, Bd. 88, Nr. 1-2: S. 5-45 [PDF, 1MB]

Domshlak, Carmel; Hüllermeier, Eyke

; Kaci, Souhila und Prade, Henri (2011): Preferences in AI: An overview. In: Artificial Intelligence, Bd. 175, Nr. 7-8: S. 1037-1052

Donner-Banzhoff, Norbert; Haasenritter, Jörg; Hüllermeier, Eyke

; Viniol, Annika; Bösner, Stefan und Becker, Annette (2014): The comprehensive diagnostic study is suggested as a design to model the diagnostic process. In: Journal of Clinical Epidemiology, Bd. 67, Nr. 2: S. 124-132 [PDF, 1MB]

Dubois, Didier; Hüllermeier, Eyke

und Prade, Henri (2006): Fuzzy methods for case-based recommendation and decision support. In: Journal of Intelligent Information Systems, Bd. 27, Nr. 2: S. 95-115

Fober, Thomas; Glinca, Gerghei; Klebe, Gerhard und Hüllermeier, Eyke

(2011): Superposition and Alignment of Labeled Point Clouds. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Bd. 8, Nr. 6: S. 1653-1666

Fober, Thomas; Mernberger, Marco; Klebe, Gerhard und Hüllermeier, Eyke

(2012): Fingerprint Kernels for Protein Structure Comparison. In: Molecular Informatics, Bd. 31, Nr. 6-7: S. 443-452

Fumagalli, Fabian

; Muschalik, Maximilian

; Hüllermeier, Eyke

und Hammer, Barbara

(September 2023): Incremental permutation feature importance (iPFI): towards online explanations on data streams. In: Machine Learning, Bd. 112, Nr. 12: S. 4863-4903 [PDF, 10MB]

Fürnkranz, Johannes; Hüllermeier, Eyke

; Cheng, Weiwei und Park, Sang-Hyeun (2012): Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. In: Machine Learning, Bd. 89, Nr. 1-2: S. 123-156

Garcia-Jimenez, Santiago; Bustince, Humberto; Hüllermeier, Eyke

; Mesiar, Radko; Pal, Nikhil R. und Pradera, Ana (2015): Overlap Indices: Construction of and Application to Interpolative Fuzzy Systems. In: IEEE Transactions on Fuzzy Systems, Bd. 23, Nr. 4: S. 1259-1273

Haas, Stefan

; Hegestweiler, Konstantin; Rapp, Michael

; Muschalik, Maximilian

und Hüllermeier, Eyke

(Oktober 2024): Stakeholder-centric explanations for black-box decisions: an XAI process model and its application to automotive goodwill assessments. In: Frontiers in Artificial Intelligence, Bd. 7 [PDF, 11MB]

Haas, Stefan

und Hüllermeier, Eyke

(Juni 2024): Conformalized prescriptive machine learning for uncertainty-aware automated decision making: the case of goodwill requests. In: International Journal of Data Science and Analytics [PDF, 1MB]

Haasenritter, Jörg; Viniol, Annika; Becker, Annette; Bösner, Stefan; Hüllermeier, Eyke

; Senge, Robin und Donner-Banzhoff, Norbert (2013): Diagnose im Kontext – eine erweiterte Perspektive. In: Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, Bd. 107, Nr. 9-10: S. 585-591

Haddenhorst, Björn

; Bengs, Viktor und Hüllermeier, Eyke

(Juli 2021): On testing transitivity in online preference learning. In: Machine Learning, Bd. 110, Nr. 8: S. 2063-2084 [PDF, 2MB]

Haddenhorst, Björn; Hüllermeier, Eyke

und Kolb, Martin (April 2020): Generalized transitivity: A systematic comparison of concepts with an application to preferences in the Babington Smith model. In: International Journal of Approximate Reasoning, Bd. 119: S. 373-407

Heid, Stefan; Hanselle, Jonas

; Fürnkranz, Johannes und Hüllermeier, Eyke

(2024): Learning decision catalogues for situated decision making: The case of scoring systems. In: International Journal of Approximate Reasoning, Bd. 171, 109190 [PDF, 1MB]

Heid, Stefan; Weber, Daniel; Bode, Henrik; Hüllermeier, Eyke

und Wallscheid, Oliver

(Oktober 2020): OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. In: Journal of Open Source Software (JOSS), Bd. 5, Nr. 54, 2435 [PDF, 388kB]

Heid, Stefan; Wever, Marcel

und Hüllermeier, Eyke

(Oktober 2024): Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In: Journal of Data Mining & Digital Humanities [PDF, 597kB]

Heid, Stefan; Wever, Marcel Dominik

und Hüllermeier, Eyke

(21. Oktober 2024): Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In: Journal of Data Mining & Digital Humanities [PDF, 597kB]

Heider, Dominik; Senge, Robin; Cheng, Weiwei und Hüllermeier, Eyke

(2013): Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction. In: Bioinformatics, Bd. 29, Nr. 16: S. 1946-1952

Henzgen, Sascha und Hüllermeier, Eyke

(2019): Mining Rank Data. In: ACM Transactions on Knowledge Discovery from Data, Bd. 13, Nr. 6: S. 1-36

Henzgen, Sascha; Strickert, Marc und Hüllermeier, Eyke

(2014): Visualization of evolving fuzzy rule-based systems. In: Evolving Systems, Bd. 5, Nr. 3: S. 175-191

Hesse, Michael; Timmermann, Julia; Hüllermeier, Eyke

und Trächtler, Ansgar (2018): A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. In: Procedia Manufacturing, Bd. 24: S. 15-20 [PDF, 503kB]

Hirsch, Oliver; Bösner, Stefan; Hüllermeier, Eyke

; Senge, Robin; Dembczynski, Krzysztof und Donner-Banzhoff, Norbert (2011): Multivariate modeling to identify patterns in clinical data: the example of chest pain. In: BMC Medical Research Methodology, Bd. 11, 155 [PDF, 3MB]

Hoffmann, Frank; Hüllermeier, Eyke

und Kroll, Andreas (2012): Ausgewählte Beiträge des GMA-Fachausschusses 5.14 “Computational Intelligence”. In: at - Automatisierungstechnik, Bd. 60, Nr. 10: S. 587-588

Hühn, Jens und Hüllermeier, Eyke

(2009): FURIA: an algorithm for unordered fuzzy rule induction. In: Data Mining and Knowledge Discovery, Bd. 19, Nr. 3: S. 293-319

Hüllermeier, Eyke

(2011): Fuzzy sets in machine learning and data mining. In: Applied Soft Computing, Bd. 11, Nr. 2: S. 1493-1505 [PDF, 242kB]

Hüllermeier, Eyke

und Słowiński, Roman

(2024): Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies–part I. In: 4OR, Bd. 22, Nr. 2: S. 179-209 [PDF, 710kB]

Hüllermeier, Eyke

und Słowiński, Roman

(2024): Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies—part II. In: 4OR - A Quarterly Journal of Operations Research, Bd. 22, Nr. 3: S. 313-349 [PDF, 654kB]

Hüllermeier, Eyke

und Waegeman, Willem (März 2021): Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. In: Machine Learning, Nr. 3: S. 457-506 [PDF, 4MB]

Hüllermeier, Eyke

; Wever, Marcel; Loza Mencia, Eneldo; Fürnkranz, Johannes und Rapp, Michael

(Februar 2022): A flexible class of dependence-aware multi-label loss functions. In: Machine Learning, Bd. 111, Nr. 2: S. 713-737 [PDF, 1MB]

Hüllermeier, Eyke

(2015): Does machine learning need fuzzy logic? In: Fuzzy Sets and Systems, Bd. 281: S. 292-299

Hüllermeier, Eyke

(2014): Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. In: International Journal of Approximate Reasoning, Bd. 55, Nr. 7: S. 1519-1534

Hüllermeier, Eyke

(2014): Rejoinder on “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization”. In: International Journal of Approximate Reasoning, Bd. 55, Nr. 7: S. 1609-1613

Hüllermeier, Eyke

(2015): From knowledge-based to data-driven fuzzy modeling. In: Informatik-Spektrum, Bd. 38, Nr. 6: S. 500-509

Hüllermeier, Eyke

(2011): Fuzzy machine learning and data mining. In: WIREs: Data Mining and Knowledge Discovery, Bd. 1, Nr. 4: S. 269-283

Hüllermeier, Eyke

und Fürnkranz, Johannes (2010): On predictive accuracy and risk minimization in pairwise label ranking. In: Journal of Computer and System Sciences, Bd. 76, Nr. 1: S. 49-62

Hüllermeier, Eyke

; Rifqi, Maria; Henzgen, Sascha und Senge, Robin (2012): Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures. In: IEEE Transactions on Fuzzy Systems, Bd. 20, Nr. 3: S. 546-556

Hüllermeier, Eyke

und Vanderlooy, S. (2009): Why Fuzzy Decision Trees are Good Rankers. In: IEEE Transactions on Fuzzy Systems, Bd. 17, Nr. 6: S. 1233-1244

Hüllermeier, Eyke

und Vanderlooy, Stijn (2010): Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. In: Pattern Recognition, Bd. 43, Nr. 1: S. 128-142

Iliadis, Dimitrios

; Wever, Marcel

; De Baets, Bernard

und Waegeman, Willem

(November 2024): Hyperparameter optimization of two-branch neural networks in multi-target prediction. In: Applied Soft Computing, Bd. 165: S. 111957 [PDF, 849kB]

Javanmardi, Alireza; Aimiyekagbon, Osarenren Kennedy; Bender, Amelie; Kimotho, James Kuria; Sextro, Walter und Hüllermeier, Eyke

(2024): Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions. In: PHM Society European Conference, Bd. 8, Nr. 1: S. 9 [PDF, 1MB]

Javanmardi, Alireza und Hüllermeier, Eyke

(24. Juli 2023): Conformal Prediction Intervals for Remaining Useful Lifetime Estimation. In: International Journal of Prognostics and Health Management, Bd. 14, Nr. 2 [PDF, 2MB]

Karimi, Iman; Hüllermeier, Eyke

und Meskouris, Konstantin (2006): A fuzzy-probabilistic earthquake risk assessment system. In: Soft Computing, Bd. 11, Nr. 3: S. 229-238

Krempl, Georg; Žliobaite, Indre; Brzeziński, Dariusz; Hüllermeier, Eyke

; Last, Mark; Lemaire, Vincent; Noack, Tino; Shaker, Ammar; Sievi, Sonja; Spiliopoulou, Myra und Stefanowski, Jerzy (2014): Open challenges for data stream mining research. In: ACM SIGKDD Explorations Newsletter, Bd. 16, Nr. 1: S. 1-10

Krotzky, Timo; Fober, Thomas; Hüllermeier, Eyke

und Klebe, Gerhard (2014): Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Bd. 11, Nr. 5: S. 878-890

Leinweber, Matthias; Fober, Thomas; Strickert, Marc; Baumgartner, Lars; Klebe, Gerhard; Freisleben, Bernd und Hüllermeier, Eyke

(2016): CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites. In: IEEE Transactions on Knowledge and Data Engineering, Bd. 28, Nr. 6: S. 1423-1434

Lienen, Julian

und Hüllermeier, Eyke

(Juli 2021): Instance weighting through data imprecisiation. In: International Journal of Approximate Reasoning, Bd. 134: S. 1-14

Melnikov, Vitalik und Hüllermeier, Eyke

(2018): On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. In: Machine Learning, Bd. 107, Nr. 8-10: S. 1537-1560

Melnikov, Vitalik; Hüllermeier, Eyke

; Kaimann, Daniel; Frick, Bernd und Gupta, Pritha (2016): Pairwise versus Pointwise Ranking: A Case Study. In: Schedae Informaticae, Bd. 25: S. 73-83 [PDF, 1MB]

Mernberger, Marco; Klebe, Gerhard und Hüllermeier, Eyke

(2011): SEGA: Semiglobal Graph Alignment for Structure-Based Protein Comparison. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Bd. 8, Nr. 5: S. 1330-1343

Mernberger, Marco; Moog, Daniel; Stork, Simone; Zauner, Stefan; Maier, Uwe G. und Hüllermeier, Eyke

(2014): Protein Sub-Cellular Localization Prediction for Special Compartments via Optimized Time Series Distances. In: Journal of Bioinformatics and Computational Biology, Bd. 12, Nr. 1: S. 1350016

Merten, Marie-Luis; Seemann, Nina und Wever, Marcel Dominik

(Dezember 2019): Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. In: Jahrbuch des Vereins für Niederdeutsche Sprachforschung = Niederdeutsches Jahrbuch, Bd. 142: S. 124-146

Mohr, Felix

und Wever, Marcel

(April 2023): Naive automated machine learning. In: Machine Learning, Bd. 112, Nr. 4: S. 1131-1170 [PDF, 7MB]

Mohr, Felix; Wever, Marcel

und Hüllermeier, Eyke

(2018): ML-Plan: Automated machine learning via hierarchical planning. In: Machine Learning, Bd. 107, Nr. 8-10: S. 1495-1515

Mohr, Felix

; Wever, Marcel

; Tornede, Alexander

und Hüllermeier, Eyke

(Februar 2021): Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Bd. 43, Nr. 9: S. 3055-3066

Montañes, Elena; Senge, Robin; Barranquero, Jose; Ramón Quevedo, José; José del Coz, Juan und Hüllermeier, Eyke

(2014): Dependent binary relevance models for multi-label classification. In: Pattern Recognition, Bd. 47, Nr. 3: S. 1494-1508

Mortier, Thomas

; Wydmuch, Marek; Dembczyński, Krzysztof; Hüllermeier, Eyke

und Waegeman, Willem (Mai 2021): Efficient set-valued prediction in multi-class classification. In: Data Mining and Knowledge Discovery, Bd. 35, Nr. 4: S. 1435-1469 [PDF, 815kB]

Muschalik, Maximilian

; Fumagalli, Fabian

; Hammer, Barbara

und Hüllermeier, Eyke

(Juli 2022): Agnostic Explanation of Model Change based on Feature Importance. In: KI - Künstliche Intelligenz, Bd. 36, Nr. 3-4: S. 211-224 [PDF, 1MB]

Nguyen, Vu-Linh

und Hüllermeier, Eyke

(2. November 2021): Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence. In: Journal of Artificial Intelligence Research, Bd. 72: S. 613-665 [PDF, 1MB]

Nguyen, Vu-Linh; Shaker, Mohammad Hossein und Hüllermeier, Eyke

(Januar 2022): How to measure uncertainty in uncertainty sampling for active learning. In: Machine Learning, Bd. 111, Nr. 1: S. 89-122 [PDF, 7MB]

Pfannschmidt, Karlson

; Gupta, Pritha

; Haddenhorst, Björn und Hüllermeier, Eyke

(Januar 2022): Learning context-dependent choice functions. In: International Journal of Approximate Reasoning, Bd. 140: S. 116-155 [PDF, 4MB]

Pfeffer, Patrick; Fober, Thomas; Hüllermeier, Eyke

und Klebe, Gerhard (2010): GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm. In: Journal of Chemical Information and Modeling, Bd. 50, Nr. 9: S. 1644-1659

Platenius, Marie C.; Shaker, Ammar; Becker, Matthias; Hüllermeier, Eyke

und Schafer, Wilhelm (2017): Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. In: IEEE Transactions on Software Engineering, Bd. 43, Nr. 8: S. 739-759

Ramaswamy, Arunselvan und Hüllermeier, Eyke

(September 2021): Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. In: IEEE Transactions on Artificial Intelligence: S. 1

Rapp, Michael

; Fürnkranz, Johannes und Hüllermeier, Eyke

(27. Juli 2023): On the efficient implementation of classification rule learning. In: Advances in Data Analysis and Classification, Bd. 18: S. 851-892 [PDF, 1MB]

Richter, Cedric; Hüllermeier, Eyke

; Jakobs, Marie-Christine und Wehrheim, Heike (April 2020): Algorithm selection for software validation based on graph kernels. In: Automated Software Engineering, Bd. 27, Nr. 1-2: S. 153-186 [PDF, 2MB]

Riemenschneider, Mona; Senge, Robin; Neumann, Ursula; Hüllermeier, Eyke

und Heider, Dominik (2016): Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. In: BioData Mining, Bd. 9, 10 [PDF, 392kB]

Rohlfing, Katharina J.

; Cimiano, Philipp; Scharlau, Ingrid

; Matzner, Tobias; Buhl, Heike M.; Buschmeier, Hendrik

; Esposito, Elena

; Grimminger, Angela

; Hammer, Barbara; Hab-Umbach, Reinhold

; Horwath, Ilona

; Hüllermeier, Eyke

; Kern, Friederike; Kopp, Stefan

; Thommes, Kirsten

; Ngonga Ngomo, Axel-Cyrille; Schulte, Carsten; Wachsmuth, Henning; Wagner, Petra

und Wrede, Britta (September 2021): Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. In: IEEE Transactions on Cognitive and Developmental Systems, Bd. 13, Nr. 3: S. 717-728 [PDF, 1MB]

Rohlfing, Katharina J.

; Leonardi, Giuseppe

; Nomikou, Iris

; Raczaszek-Leonardi, Joanna

und Hüllermeier, Eyke

(Juni 2020): Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. In: IEEE Transactions on Cognitive and Developmental Systems, Bd. 12, Nr. 2: S. 260-271

Ruiz, M. Dolores und Hüllermeier, Eyke

(2012): A formal and empirical analysis of the fuzzy gamma rank correlation coefficient. In: Information Sciences, Bd. 206: S. 1-17

Schede, Elias; Brandt, Jasmin; Tornede, Alexander

; Wever, Marcel

; Bengs, Viktor

; Hüllermeier, Eyke

und Tierney, Kevin (Oktober 2022): A Survey of Methods for Automated Algorithm Configuration. In: Journal of Artificial Intelligence Research, Bd. 75: S. 425-487 [PDF, 852kB]

Schäfer, Dirk und Hüllermeier, Eyke

(2018): Dyad ranking using Plackett–Luce models based on joint feature representations. In: Machine Learning, Bd. 107, Nr. 5: S. 903-941

Senge, Robin; Bösner, Stefan; Dembczyński, Krzysztof; Haasenritter, Jörg; Hirsch, Oliver; Donner-Banzhoff, Norbert und Hüllermeier, Eyke

(2014): Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. In: Information Sciences, Bd. 255: S. 16-29

Senge, Robin; Fober, Thomas; Nasiri, Maryam und Hüllermeier, Eyke

(2012): Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. In: at - Automatisierungstechnik, Bd. 60, Nr. 10: S. 622-629

Senge, Robin und Hüllermeier, Eyke

(2015): Fast Fuzzy Pattern Tree Learning for Classification. In: IEEE Transactions on Fuzzy Systems, Bd. 23, Nr. 6: S. 2024-2033

Senge, Robin und Hüllermeier, Eyke

(2011): Top-Down Induction of Fuzzy Pattern Trees. In: IEEE Transactions on Fuzzy Systems, Bd. 19, Nr. 2: S. 241-252

Shaker, Ammar und Hüllermeier, Eyke

(Juni 2021): TSK-Streams: learning TSK fuzzy systems for regression on data streams. In: Data Mining and Knowledge Discovery, Nr. 5: S. 1941-1971 [PDF, 598kB]

Shaker, Ammar und Hüllermeier, Eyke

(2015): Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. In: Neurocomputing, Bd. 150, Nr. A: S. 250-264

Shaker, Ammar und Hüllermeier, Eyke

(2014): Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. In: International Journal of Applied Mathematics and Computer Science, Bd. 24, Nr. 1: S. 199-212 [PDF, 506kB]

Shaker, Ammar und Hüllermeier, Eyke

(2012): IBLStreams: a system for instance-based classification and regression on data streams. In: Evolving Systems, Bd. 3, Nr. 4: S. 235-249

Shaker, Ammar; Senge, Robin und Hüllermeier, Eyke

(2013): Evolving fuzzy pattern trees for binary classification on data streams. In: Information Sciences, Bd. 220: S. 34-45

Stock, Michiel; Fober, Thomas; Hüllermeier, Eyke

; Glinca, Serghei; Klebe, Gerhard; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard und Waegeman, Willem (2014): Identification of Functionally Related Enzymes by Learning-to-Rank Methods. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Bd. 11, Nr. 6: S. 1157-1169

Strickert, Marc; Bunte, Kerstin; Schleif, Frank-Michael und Hüllermeier, Eyke

(2014): Correlation-based embedding of pairwise score data. In: Neurocomputing, Bd. 141: S. 97-109

Tagne, Vidal Kamdem; Fotso, Siméon; Fono, Louis Aimé und Hüllermeier, Eyke

(2019): Choice Functions Generated by Mallows and Plackett–Luce Relations. In: New Mathematics and Natural Computation, Bd. 15, Nr. 2: S. 191-213

Tehrani, Ali Fallah; Cheng, Weiwei; Dembczyński, Krzysztof und Hüllermeier, Eyke

(2012): Learning monotone nonlinear models using the Choquet integral. In: Machine Learning, Bd. 89, Nr. 1-2: S. 183-211

Tehrani, Ali Fallah; Cheng, Weiwei und Hüllermeier, Eyke

(2012): Preference Learning Using the Choquet Integral: The Case of Multipartite Ranking. In: IEEE Transactions on Fuzzy Systems, Bd. 20, Nr. 6: S. 1102-1113

Tornede, Alexander

; Gehring, Lukas; Tornede, Tanja

; Wever, Marcel

und Hüllermeier, Eyke

(2023): Algorithm selection on a meta level. In: Machine Learning, Bd. 112, Nr. 4: S. 1253-1286 [PDF, 1MB]

Tornede, Tanja

; Tornede, Alexander

; Fehring, Lukas

; Gehring, Lukas; Graf, Helena

; Hanselle, Jonas

; Mohr, Felix

und Wever, Marcel

(20. April 2023): PyExperimenter: Easily distribute experiments and track results. In: Journal of Open Source Software, Bd. 8, Nr. 84: S. 5149 [PDF, 236kB]

Tornede, Tanja

; Tornede, Alexander

; Hanselle, Jonas; Mohr, Felix

; Wever, Marcel

und Hüllermeier, Eyke

(2023): Towards Green Automated Machine Learning: Status Quo and Future Directions. In: Journal of Artificial Intelligence Research, Bd. 77: S. 427-457 [PDF, 299kB]

Verwimp, Eli; Aljundi, Rahaf; Ben-David, Shai; Bethge, Matthias; Cossu, Andrea; Gepperth, Alexander; Hayes, Tyler L.; Hüllermeier, Eyke

; Kanan, Christopher; Kudithipudi, Dhireesha; Lampert, Christoph H.; Mundt, Martin; Pascanu, Razvan; Popescu, Adrian; Tolias, Andreas S.; van de Weijer, Joost; Liu, Bing; Lomonaco, Vincenzo; Tuytelaars, Tinne und van de Ven, Gido M. (März 2024): Continual Learning: Applications and the Road Forward. In: Transactions on Machine Learning Research [PDF, 1MB]

Waegeman, Willem

; Dembczyński, Krzysztof; Jachnik, Arkadiusz; Cheng, Weiwei und Hüllermeier, Eyke

(November 2014): On the Bayes-Optimality of F-Measure Maximizers. In: Journal of Machine Learning Research, Bd. 15, 103: S. 3513-3568 [PDF, 676kB]

Waegeman, Willem

; Dembczyński, Krzysztof und Hüllermeier, Eyke

(2019): Multi-target prediction: a unifying view on problems and methods. In: Data Mining and Knowledge Discovery, Bd. 33, Nr. 2: S. 293-324

Weber, Daniel

; Heid, Stefan; Bode, Henrik; Lange, Jarren H.; Hüllermeier, Eyke

und Wallscheid, Oliver

(Februar 2021): Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. In: IEEE Access, Bd. 9: S. 35654-35669 [PDF, 2MB]

Weskamp, Nils; Hüllermeier, Eyke

und Klebe, Gerhard (2009): Merging chemical and biological space: Structural mapping of enzyme binding pocket space. In: Proteins: Structure, Function, and Bioinformatics, Bd. 76, Nr. 2: S. 317-330

Wever, Marcel

; Tornede, Alexander

; Mohr, Felix

und Hüllermeier, Eyke

(September 2021): AutoML for Multi-Label Classification: Overview and Empirical Evaluation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Bd. 43, Nr. 9: S. 3037-3054 [PDF, 2MB]

Wever, Marcel

; van Rooijen, Lorijn und Hamann, Heiko (Juni 2020): Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. In: Evolutionary Computation, Bd. 28, Nr. 2: S. 165-193

Paper

Ahmadi Fahandar, Mohsen und Hüllermeier, Eyke

(2017): Learning to Rank based on Analogical Reasoning.

Hüllermeier, Eyke

(2017): From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection. arxiv.org

Mohr, Felix; Wever, Marcel Dominik

und Hüllermeier, Eyke

(2018): Automated Machine Learning Service Composition.

Buchbeitrag

Abdel-Aziz, Amira und Hüllermeier, Eyke

(2015): Case Base Maintenance in Preference-Based CBR. In: Hüllermeier, Eyke und Minor, Mirjam (Hrsg.): Case-Based Reasoning Research and Development. 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany, September 28-30, 2015. Proceedings. Lecture Notes in Computer Science, Bd. 9343. Cham: Springer. S. 1-14

Abdel-Aziz, Amira; Strickert, Marc und Hüllermeier, Eyke

(2014): Learning Solution Similarity in Preference-Based CBR. In: Lamontagne, Luc und Plaza, Enric (Hrsg.): Case-Based Reasoning Research and Development. 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings. Lecture Notes in Computer Science, Bd. 8765. Cham: Springer. S. 17-31

Ahmadi Fahandar, Mohsen und Hüllermeier, Eyke

(2019): Analogy-Based Preference Learning with Kernels. In: Benzmüller, Christoph und Stuckenschmidt, Heiner (Hrsg.): KI 2019: Advances in Artificial Intelligence. 42nd German Conference on AI, Kassel, Germany, September 23–26, 2019, Proceedings. Lecture Notes in Computer Science, Bd. 11793. Cham: Springer. S. 34-47

Ahmadi Fahandar, Mohsen und Hüllermeier, Eyke

(2019): Feature Selection for Analogy-Based Learning to Rank. In: Kralj Novak, Petra; Šmuc, Tomislav und Džeroski, Sašo (Hrsg.): Discovery Science. 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings. Lecture Notes in Computer Science, Bd. 11828. Cham: Springer. S. 279-289

Beringer, Jürgen und Hüllermeier, Eyke

(2007): Fuzzy Clustering of Parallel Data Streams. In: Oliveira, Jose Valente de und Pedrycz, Witold (Hrsg.): Advances in Fuzzy Clustering and its Applications. Chichester: Wiley. S. 333-352

Beringer, Jürgen und Hüllermeier, Eyke

(2007): An Efficient Algorithm for Instance-Based Learning on Data Streams. In: Perner, Petra (Hrsg.): Advances in Data Mining. Theoretical Aspects and Applications. 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007. Proceedings. Lecture Notes in Computer Science, Bd. 4597. Berlin, Heidelberg: Springer. S. 34-48

Brinker, Klaus und Hüllermeier, Eyke

(April 2020): A Reduction of Label Ranking to Multiclass Classification. In: Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. Lecture Notes in Computer Science, Bd. 11908. Cham: Springer. S. 204-219

Brinker, Klaus und Hüllermeier, Eyke

(2006): Case-Based Label Ranking. In: Fürnkranz, Johannes; Scheffer, Tobias und Spiliopoulou, Myra (Hrsg.): Machine Learning: ECML 2006. 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings. Lecture Notes in Computer Science, Bd. 4212. Berlin, Heidelberg: Springer. S. 566-573

Brinker, Klaus und Hüllermeier, Eyke

(2007): Label Ranking in Case-Based Reasoning. In: Weber, Rosina O. und Richter, Michael M. (Hrsg.): Case-Based Reasoning Research and Development. 7th International Conference on Case-Based Reasoning, ICCBR 2007 Belfast, Northern Ireland, UK, August 13-16, 2007 Proceedings. Lecture Notes in Computer Science, Bd. 4626. Berlin, Heidelberg: Springer. S. 77-91

Busa-Fekete, Róbert und Hüllermeier, Eyke

(2014): A Survey of Preference-Based Online Learning with Bandit Algorithms. In: Auer, Peter; Clark, Alexander; Zeugmann, Thomas und Zilles, Sandra (Hrsg.): Algorithmic Learning Theory. 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. Lecture Notes in Computer Science, Bd. 8776. Cham: Springer. S. 18-39

Cheng, Weiwei; Fürnkranz, Johannes; Hüllermeier, Eyke

und Park, Sang-Hyeun (2011): Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning. In: Gunopulos, Dimitrios; Hofmann, Thomas; Malerba, Donato und Vazirgiannis, Michalis (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I. Lecture Notes in Computer Science, Bd. 6911. Berlin, Heidelberg: Springer. S. 312-327

Cheng, Weiwei und Hüllermeier, Eyke

(2008): Learning Similarity Functions from Qualitative Feedback. In: Althoff, Klaus-Dieter; Bergmann, Ralph; Minor, Mirjam und Hanft, Alexandre (Hrsg.): Advances in Case-Based Reasoning. 9th European Conference, ECCBR 2008, Trier, Germany, September 1-4, 2008. Proceedings. Lecture Notes in Computer Science, Bd. 5239. Berlin, Heidelberg: Springer. S. 120-134

Cheng, Weiwei und Hüllermeier, Eyke

(2009): Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. In: Buntine, Wray; Grobelnik, Marko; Mladenić, Dunja und Shawe-Taylor, John (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 5781. Berlin, Heidelberg: Springer. S. 6

Cheng, Weiwei und Hüllermeier, Eyke

(2012): Probability Estimation for Multi-class Classification Based on Label Ranking. In: Flach, Peter A.; Bie, Tijl De und Cristianini, Nello (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II. Lecture Notes in Computer Science, Bd. 7524. Berlin, Heidelberg: Springer. S. 83-98

Cheng, Weiwei und Hüllermeier, Eyke

(2009): A New Instance-Based Label Ranking Approach Using the Mallows Model. In: Yu, Wen; He, Haibo und Zhang, Nian (Hrsg.): (2009): Advances in Neural Networks – ISNN 2009 : 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part I. Lecture Notes in Computer Science, Bd. 5551. Berlin, Heidelberg: Springer. S. 707-716

Cheng, Weiwei; Rademaker, Michaël; De Baets, Bernard und Hüllermeier, Eyke

(2010): Predicting Partial Orders: Ranking with Abstention. In: Balcázar, José Luis; Bonchi, Francesco; Gionis, Aristides und Sebag, Michèle (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 6321. Berlin, Heidelberg: Springer. S. 215-230

Couso, Inés; Dubois, Didier und Hüllermeier, Eyke

(2017): Maximum Likelihood Estimation and Coarse Data. In: Moral, Serafín; Pivert, Olivier; Sánchez, Daniel und Marín, Nicolás (Hrsg.): Scalable Uncertainty Management. 11th International Conference, SUM 2017, Granada, Spain, October 4-6, 2017, Proceedings. Lecture Notes in Computer Science, Bd. 10564. Cham: Springer. S. 3-16

Couso, Inés und Hüllermeier, Eyke

(2017): Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators. In: Frontiers in Computational Intelligence. Studies in Computational Intelligence, Bd. 739. Cham: Springer. S. 31-46

Dembczyński, Krzysztof; Kotłowski, Wojciech; Waegeman, Willem; Busa-Fekete, Róbert und Hüllermeier, Eyke

(2016): Consistency of Probabilistic Classifier Trees. In: Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe und Vreeken, Jilles (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II. Lecture Notes in Computer Science, Bd. 9852. Cham: Springer. S. 511-526 [PDF, 322kB]

Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei und Hüllermeier, Eyke

(2010): Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss. In: Balcázar, José Luis; Bonchi, Francesco; Gionis, Aristides und Sebag, Michèle (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 6321. Berlin, Heidelberg: Springer. S. 280-295

Dembczyński, Krzysztof; Waegeman, Willem und Hüllermeier, Eyke

(August 2012): An Analysis of Chaining in Multi-Label Classification. In: De Raedt, Luc; Bessiere, Christian; Dubois, Didier; Doherty, Patrick; Frasconi, Paolo; Heintz, Fredrik und Lucas, Peter (Hrsg.): ECAI 2012 : 20th European Conference on Artificial Intelligence, 27 - 31 August 2012, Montpellier, France. Frontiers in Artificial Intelligence and Applications, Bd. 242. Amsterdam: IOS Press. S. 294-299 [PDF, 258kB]

Dubois, Didier und Hüllermeier, Eyke

(Juli 2005): A Notion of Comparative Probabilistic Entropy Based on the Possibilistic Specificity Ordering. In: Symbolic and Quantitative Approaches to Reasoning with Uncertainty. 8th European Conference, ECSQARU 2005, Barcelona, Spain, July 6-8, 2005. Proceedings. Lecture Notes in Computer Science, Bd. 3571. Berlin, Heidelberg: Springer. S. 848-859

Dubois, Didier; Hüllermeier, Eyke

und Prade, Henri (2003): A Note on Quality Measures for Fuzzy Association Rules. In: Bilgiç, Taner; De Baets, Bernard und Okyay Kaynak (Hrsg.): Fuzzy Sets and Systems — IFSA 2003. 10th International Fuzzy Systems Association World Congress Istanbul, Turkey, June 30 – July 2, 2003 Proceedings. Lecture Notes in Computer Science, Bd. 2715. Berlin, Heidelberg: Springer. S. 346-353

Dubois, Didier; Hüllermeier, Eyke

und Prade, Henri (2003): Flexible Control of Case-Based Prediction in the Framework of Possibility Theory. In: Blanzieri, Enrico und Portinale, Luigi (Hrsg.): Advances in Case-Based Reasoning. 5th European Workshop, EWCBR 2000 Trento, Italy, September 6–9, 2000 Proceedings. Lecture Notes in Computer Science, Bd. 1898. Berlin, Heidelberg: Springer. S. 61-73

Dubois, Didier; Hüllermeier, Eyke

und Prade, Henri (2003): Possibilistic case-based decisions. In: Lesage, Cédric und Cottrell, Marie (Hrsg.): Connectionist Approaches in Economics and Management Sciences. Advances in Computational Management Science, Bd. 6. Boston, MA: Springer. S. 31-48

El Mesaoudi-Paul, Adil; Weiß, Dimitri; Bengs, Viktor; Hüllermeier, Eyke

und Tierney, Kevin (Mai 2020): Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Kotsireas, Ilias S. und Pardalos, Panos M. (Hrsg.): Learning and Intelligent Optimization. 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers. Lecture Notes in Computer Science, Bd. 12096. Cham: Springer. S. 216-232

Ewerth, Ralph; Balz, Alexander; Gehlhaar, Jan; Dembczyński, Krzysztof und Hüllermeier, Eyke

(November 2015): Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches. In: Hoffmann, Frank und Hüllermeier, Eyke (Hrsg.): Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Bd. 54. Karlsruhe: KIT Scientific Publishing. S. 235-239 [PDF, 494kB]

Fober, Thomas; Klebe, Gerhard und Hüllermeier, Eyke

(2013): Local Clique Merging: An Extension of the Maximum Common Subgraph Measure with Applications in Structural Bioinformatics. In: Lausen, Berthold; Poel, Dirk van den und Ultsch, Alfred (Hrsg.): Algorithms from and for Nature and Life. Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, Cham: Springer. S. 279-286

Fürnkranz, Johannes und Hüllermeier, Eyke

(2010): Preference Learning and Ranking by Pairwise Comparison. In: Fürnkranz, Johannes und Hüllermeier, Eyke (Hrsg.): Preference Learning. Berlin, Heidelberg: Springer. S. 65-82

Fürnkranz, Johannes und Hüllermeier, Eyke

(2010): Preference Learning: An Introduction. In: Fürnkranz, Johannes und Hüllermeier, Eyke (Hrsg.): Preference Learning. Berlin: Springer. S. 1-17

Fürnkranz, Johannes und Hüllermeier, Eyke

(September 2003): Pairwise Preference Learning and Ranking. In: Lavrač, Nada; Gamberger, Dragan; Blockeel, Hendrik und Todorovski, Ljupčo (Hrsg.): Machine Learning: ECML 2003. 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003. Proceedings. Lecture Notes in Computer Science, Bd. 2837. Berlin, Heidelberg: Springer. S. 145-156

Fürnkranz, Johannes; Hüllermeier, Eyke

und Vanderlooy, Stijn (2009): Binary Decomposition Methods for Multipartite Ranking. In: Buntine, Wray; Grobelnik, Marko; Mladenić, Dunja und Shawe-Taylor, John (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 5781. Berlin, Heidelberg: Springer. S. 359-374

Hanselle, Jonas; Tornede, Alexander

; Wever, Marcel

und Hüllermeier, Eyke

(Mai 2021): Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. In: Karlapalem, Kamal; Cheng, Hong; Ramakrishnan, Naren; Agrawal, R. K.; Krishna Reddy, P.; Srivastava, Jaideep und Chakraborty, Tanmoy (Hrsg.): (2021): Advances in Knowledge Discovery and Data Mining 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 12712. Cham: Springer. S. 152-163 [PDF, 532kB]

Henzgen, Sascha und Hüllermeier, Eyke

(Dezember 2013): Weighted Rank Correlation Measures Based on Fuzzy Order Relations. In: Hoffmann, Frank und Hüllermeier, Eyke (Hrsg.): Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5. - 6. Dezember 2013. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Bd. 46. Karlsruhe: KIT Scientific Publishing. S. 227-236 [PDF, 1MB]

Henzgen, Sascha und Hüllermeier, Eyke

(2015): Weighted Rank Correlation: A Flexible Approach Based on Fuzzy Order Relations. In: Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science, Bd. 9285. S. 422-437

Henzgen, Sascha und Hüllermeier, Eyke

(2014): Mining Rank Data. In: Džeroski, Sašo; Panov, Panče; Kocev, Dragi und Todorovski, Ljupčo (Hrsg.): Discovery Science. 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. Lecture Notes in Computer Science, Bd. 8777. Cham: Springer. S. 123-134

Henzgen, Sascha; Strickert, Marc und Hüllermeier, Eyke

(2013): Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems. In: Burduk, Robert; Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał und Zolnierek, Andrzej (Hrsg.): Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, Bd. 226. S. 279-288

Hühn, Jens Christian und Hüllermeier, Eyke

(2010): An Analysis of the FURIA Algorithm for Fuzzy Rule Induction. In: Koronacki, Jacek; Raś, Zbigniew W.; Wierzchoń, Sławomir T. und Kacprzyk, Janusz (Hrsg.): Advances in Machine Learning I. Dedicated to the Memory of Professor Ryszard S. Michalski. Studies in Computational Intelligence, Bd. 262. Berlin, Heidelberg: Springer. S. 321-344

Hüllermeier, Eyke

(2001): Implication-Based Fuzzy Association Rules. In: Principles of Data Mining and Knowledge Discovery. 5th European Conference, PKDD 2001, Freiburg, Germany, September 3–5, 2001 Proceedings. Lecture Notes in Computer Science, Bd. 2168. Berlin, Heidelberg: Springer. S. 241-252

Hüllermeier, Eyke

(2009): On the Usefulness of Fuzzy Sets in Data Mining. In: Views on Fuzzy Sets and Systems from Different Perspectives. Philosophy and Logic, Criticisms and Applications. Studies in Fuzziness and Soft Computing, Bd. 243. Berlin, Heidelberg: Springer. S. 457-470

Hüllermeier, Eyke

(September 2002): Experience-Based Decision Making and Learning from Examples. In: Operations Research Proceedings 2001. Selected Papers of the International Conference on Operations Research (OR 2001) Duisburg, September 3–5, 2001. Berlin, Heidelberg: Springer. S. 363-370

Hüllermeier, Eyke

(Mai 2002): Exploiting similarity and experience in decision making. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems: May 12 - 17, 2002, Hilton Hawaiian Village Hotel, Honolulu, Hawaii. Vol. 1. Piscataway, NJ: IEEE. S. 729-734

Hüllermeier, Eyke

(August 2003): Regularized Learning with Flexible Constraints. In: Berthold, Michael R.; Lenz, Hans-Joachim; Bradley, Elizabeth; Kruse, Rudolf und Borgelt, Christian (Hrsg.): Advances in Intelligent Data Analysis V. 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings. Lecture Notes in Computer Science, Bd. 2810. Berlin, Heidelberg: Springer. S. 13-24

Hüllermeier, Eyke

(2003): Inducing Fuzzy Concepts through Extended Version Space Learning. In: Bilgiç, Taner; De Baets, Bernard und Kaynak, Okyay (Hrsg.): Fuzzy Sets and Systems — IFSA 2003. 10th International Fuzzy Systems Association World Congress Istanbul, Turkey, June 30 – July 2, 2003 Proceedings. Lecture Notes in Computer Science, Bd. 2715. Berlin, Heidelberg: Springer. S. 677-684

Hüllermeier, Eyke

(2003): A Method for Predicting Solutions in Case-Based Problem Solving. In: Blanzieri, Enrico und Portinale, Luigi (Hrsg.): Advances in Case-Based Reasoning. 5th European Workshop, EWCBR 2000 Trento, Italy, September 6–9, 2000 Proceedings. Lecture Notes in Computer Science, Bd. 1898. Berlin, Heidelberg: Springer. S. 124-135

Hüllermeier, Eyke

(2008): Fuzzy Methods for Data Mining and Machine Learning: State of the Art and Prospects. In: Bustince, Humberto; Herrera, Francisco und Montero, Javier (Hrsg.): Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, Bd. 220. Berlin, Heidelberg: Springer. S. 357-375

Hüllermeier, Eyke

(2003): Sequential Decision Making in Heuristic Search. In: Della Riccia, Giacomo; Dubois, Didier; Kruse, Rudolf und Lenz, Hans-J. (Hrsg.): Planning Based on Decision Theory. International Centre for Mechanical Sciences, Bd. 472. Vienna: Springer. S. 27-41

Hüllermeier, Eyke

(2010): Uncertainty in Clustering and Classification. In: Deshpande, Amol und Hunter, Anthony (Hrsg.): Scalable Uncertainty Management.4th International Conference, SUM 2010, Toulouse, France, September 27-29, 2010. Proceedings. Lecture Notes in Computer Science, Bd. 6379. Berlin, Heidelberg: Springer. S. 16-19

Hüllermeier, Eyke

(2002): Association Rules for Expressing Gradual Dependencies. In: Elomaa, Tapio; Mannila, Heikki und Toivonen, Hannu (Hrsg.): Principles of Data Mining and Knowledge Discovery. 6th European Conference, PKDD 2002 Helsinki, Finland, August 19–23, 2002 Proceedings. Lecture Notes in Computer Science, Bd. 2431. Berlin, Heidelberg: Springer. S. 200-211

Hüllermeier, Eyke

(2002): Possibilistic Induction in Decision-Tree Learning. In: Elomaa, Tapio; Mannila, Heikki und Toivonen, Hannu (Hrsg.): Machine Learning: ECML 2002.13th European Conference on Machine Learning Helsinki, Finland, August 19–23, 2002 Proceedings. Lecture Notes in Computer Science, Bd. 2430. Cham: Springer. S. 173-184

Hüllermeier, Eyke

(September 2003): Instance-Based Learning of Credible Label Sets. In: Günter, Andreas; Kruse, Rudolf und Neumann, Bernd (Hrsg.): KI 2003: Advances in Artificial Intelligence. 26th Annual German Conference on AI, KI 2003, Hamburg, Germany, September 15-18, 2003. Proceedings. Lecture Notes in Computer Science, Bd. 2821. Berlin, Heidelberg: Springer. S. 450-464

Hüllermeier, Eyke

(1999): Exploiting Similarity for Supporting Data Analysis and Problem Solving. In: Hand, David J.; Kok, Joost N. und Berthold, Michael R. (Hrsg.): Advances in Intelligent Data Analysis. Third International Symposium, IDA-99 Amsterdam, The Netherlands, August 9–11, 1999 Proceedings. Lecture Notes in Computer Science, Bd. 1642. Berlin, Heidelberg: Springer. S. 257-268

Hüllermeier, Eyke

(2008): Granular Computing in Machine Learning and Data Mining. In: Pedrycz, Witold; Skowron, Andrzej und Kreinovich, Vladik (Hrsg.): Handbook of Granular Computing. S. 889-906

Hüllermeier, Eyke

(2008): Why Fuzzy Set Theory is Useful in Data Mining. In: Poncelet, Pascal; Masseglia, Florent und Teisseire, Maguelonne (Hrsg.): Successes and New Directions in Data Mining. Hershey ; New York: Information Science Reference. S. 1-16

Hüllermeier, Eyke

(Oktober 2001): Fuzzy Association Rules: Semantic Issues and Quality Measures. In: Reusch, Bernd (Hrsg.): Computational Intelligence. Theory and Applications. International Conference, 7th Fuzzy Days Dortmund, Germany, October 1–3, 2001 Proceedings. Lecture Notes in Computer Science, Bd. 2206. Berlin, Heidelberg: Springer. S. 380-391

Hüllermeier, Eyke

(Mai 1999): A Possibilistic Formalization of Case-Based Reasoning and Decision Making. In: Reusch, Bernd (Hrsg.): Computational Intelligence. Theory and Applications International Conference, 6th Fuzzy Days Dortmund, Germany, May 25–28 1999 Proceedings. Lecture Notes in Computer Science, Bd. 1625. Berlin, Heidelberg: Springer. S. 411-420

Hüllermeier, Eyke

(2005): Fuzzy Methods in Knowledge Discovery. In: Reusch, Bernd (Hrsg.): Computational Intelligence, Theory and Applications. International Conference 8th Fuzzy Days in Dortmund, Germany, Sept. 29–Oct. 01, 2004 Proceedings. Advances in Soft Computing, Bd. 33. Berlin, Heidelberg: Springer. S. 483

Hüllermeier, Eyke

(2006): The Choquet-Integral as an Aggregation Operator in Case-Based Learning. In: Reusch, Bernd (Hrsg.): Computational Intelligence, Theory and Applications. International Conference 9th Fuzzy Days in Dortmund, Germany, Sept. 18–20, 2006 Proceedings. Berlin, Heidelberg: Springer. S. 615-627

Hüllermeier, Eyke

(2012): Fuzzy Rules in Data Mining: From Fuzzy Associations to Gradual Dependencies. In: Trillas, Enric; Bonissone, Piero P.; Magdalena, Luis und Kacprz, Janusz (Hrsg.): Combining Experimentation and Theory. A Hommage to Abe Mamdani. Studies in Fuzziness and Soft Computing, Bd. 271. S. 123-135

Hüllermeier, Eyke

und Beringer, Jürgen (September 2005): Learning from Ambiguously Labeled Examples. In: Famili, A. Fazel; Kok, Joost N.; Peña, José M.; Siebes, Arno und Feelders, Ad (Hrsg.): Advances in Intelligent Data Analysis VI. Lecture Notes in Computer Science. 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005. Proceedings, Bd. 3646. Berlin, Heidelberg: Springer. S. 168-179

Hüllermeier, Eyke

und Cheng, Weiwei (2015): Superset Learning Based on Generalized Loss Minimization. In: Machine Learning and Knowledge Discovery in Databases.European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science, Bd. 9285. Cham: Springer. S. 260-275

Hüllermeier, Eyke

und Cheng, Weiwei (2013): Preference-Based CBR: General Ideas and Basic Principles. In: Rossi, Francesca (Hrsg.): (2013): Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China, 3 - 9 August 2013, Vol. 4. Palo Alto, Calif.: AAAI Press. S. 3012-3016

Hüllermeier, Eyke

; Destercke, Sébastien und Couso, Ines (2019): Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In: Ben Amor, Nahla; Quost, Benjamin und Theobald, Martin (Hrsg.): Scalable Uncertainty Management. Lecture Notes in Computer Science, Bd. 11940. Cham: Springer. S. 266-279

Hüllermeier, Eyke

; Dubois, Didier und Prade, Henri (2002): Knowledge-Based Extrapolation of Cases: A Possibilistic Approach. In: Bouchon-Meunier, Bernadette; Gutiérrez-Ríos, Julio; Magdalena, Luis und Yager, Ronald R. (Hrsg.): Technologies for Constructing Intelligent Systems 1. Studies in Fuzziness and Soft Computing, Bd. 89. Heidelberg: Physica Verlag. S. 377-390

Hüllermeier, Eyke

und Fallah Tehrani, Ali (2012): On the VC-Dimension of the Choquet Integral. In: Greco, Salvatore; Bouchon-Meunier, Bernadette; Coletti, Gulianella; Fedrizzi, Mario; Matarazzo, Benedetto und Yager, Ronald R. (Hrsg.): Advances on Computational Intelligence. 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Catania, Italy, July 9-13, 2012. Proceedings, Part I. Communications in Computer and Information Science, Bd. 297. Berlin, Heidelberg: Springer. S. 42-50

Hüllermeier, Eyke

und Fürnkranz, Johannes (2008): Learning Preference Models from Data: On the Problem of Label Ranking and Its Variants. In: Della Riccia, Giacomo; Dubois, Didier; Kruse, Rudolf und Lenz, Hans-Joachim (Hrsg.): Preferences and Similarities. CISM International Centre for Mechanical Sciences, Bd. 504. Vienna: Springer. S. 283-304

Hüllermeier, Eyke

und Fürnkranz, Johannes (2011): Learning from Label Preferences. In: Elomaa, Tapio; Hollmén, Jaakko und Mannila, Heikki (Hrsg.): Discovery Science. 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings. Lecture Notes in Computer Science, Bd. 6926. S. 2-17

Hüllermeier, Eyke

und Fürnkranz, Johannes (September 2005): Learning Label Preferences: Ranking Error Versus Position Error. In: Fazel Famili, A.; Kok, Joost N.; Peña, José M.; Siebes, Arno und Feelders, Ad (Hrsg.): Advances in Intelligent Data Analysis VI. 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005. Proceedings. Lecture Notes in Computer Science, Bd. 3646. Berlin, Heidelberg: Springer. S. 180-191

Hüllermeier, Eyke

und Fürnkranz, Johannes (2007): On Minimizing the Position Error in Label Ranking. In: Kok, Joost N.; Koronacki, Jacek; Lopez de Mantaras, Raomon; Matwin, Stan; Mladenič, Dunja und Skowron, Andrzej (Hrsg.): Machine Learning: ECML 2007. 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. Proceedings. Lecture Notes in Computer Science, Bd. 4701. Berlin, Heidelberg: Springer. S. 583-590

Hüllermeier, Eyke

und Schlegel, Patrice (September 2011): Preference-Based CBR: First Steps toward a Methodological Framework. In: Ram, Ashwin und Wiratunga, Nirmalie (Hrsg.): Case-Based Reasoning Research and Development. 19th International Conference on Case-Based Reasoning, ICCBR 2011, London, UK, September 12-15, 2011. Proceedings. Lecture Notes in Computer Science, Bd. 6880. Berlin, Heidelberg: Springer. S. 77-91

Hüllermeier, Eyke

und Tehrani, Ali Fallah (2013): Efficient Learning of Classifiers Based on the 2-Additive Choquet Integral. In: Moewes, Christian und Nürnberger, Andreas (Hrsg.): Computational Intelligence in Intelligent Data Analysis. Studies in Computational Intelligence, Bd. 445. Berlin, Heidelberg: Springer. S. 17-29

Hüllermeier, Eyke

; Vladimirskiy, Ilya; Prados Suárez, Belén und Stauch, Eva (2008): Supporting Case-Based Retrieval by Similarity Skylines: Basic Concepts and Extensions. In: Althoff, Klaus-Dieter; Bergmann, Ralph; Minor, Mirjam und Hanft, Alexandre (Hrsg.): Advances in Case-Based Reasoning. Lecture Notes in Computer Science. 9th European Conference, ECCBR 2008, Trier, Germany, September 1-4, 2008. Proceedings, Bd. 5239. Berlin, Heidelberg: Springer. S. 240-254

Koh, Hyung-Won und Hüllermeier, Eyke

(2010): Mining Gradual Dependencies Based on Fuzzy Rank Correlation. In: Borgel, Christian (Hrsg.): Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, Bd. 77. Berlin, Heidelberg: Springer. S. 379-386

Lu, Shenzhen und Hüllermeier, Eyke

(November 2015): Locally weighted regression through data imprecisiation. In: Hoffmann, Frank und Hüllermeier, Eyke (Hrsg.): Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Bd. 54. Karlsruhe: KIT Scientific Publishing. S. 97-103 [PDF, 350kB]

Melnikov, Vitalik und Hüllermeier, Eyke

(2016): Learning to Aggregate Using Uninorms. In: Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe und Vreeken, Jilles (Hrsg.): Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, Bd. 9852. S. 756-771 [PDF, 472kB]

Melnikov, Vitalik und Hüllermeier, Eyke

(November 2017): Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Mikut, Ralf; Hüllermeier, Eyke und Hoffmann, Frank (Hrsg.): Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. Karlsruhe: KIT Scientific Publishing. S. 1-12 [PDF, 1MB]

Mencía, Eneldo Loza; Fürnkranz, Johannes; Hüllermeier, Eyke

und Rapp, Michael

(2018): Learning Interpretable Rules for Multi-Label Classification. In: Jair Escalante, Hugo; Escalera, Sergio; Guyon, Isabelle; Baró, Xavier; Güçlütürk, Yağmur; Güçlü, Umut und Gerven, Marcel van (Hrsg.): Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning, Cham: Springer. S. 81-113

Mohr, Felix; Wever, Marcel

; Tornede, Alexander

und Hüllermeier, Eyke

(September 2019): From Automated to On-The-Fly Machine Learning. In: David, Klaus; Geihs, Kurt; Lange, Martin und Stumme, Gerd (Hrsg.): (2021): Informatik 2019. 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Konferenzbeiträge, der 49. Jahrestagung der Gesellschaft für Informatik, 23.- 26.9.2019 Kassel, Deutschland. Bonn: Gesellschaft für Informatik. S. 273-274 [PDF, 123kB]

Mohr, Felix; Lettmann, Theo und Hüllermeier, Eyke

(2017): Planning with Independent Task Networks. In: Kern-Isberner, Gabriele; Fürnkranz, Johannes und Thimm, Matthias (Hrsg.): KI 2017: Advances in Artificial Intelligence. 40th Annual German Conference on AI, Dortmund, Germany, September 25–29, 2017, Proceedings. Lecture Notes in Computer Science, Bd. 10505. Cham: Springer. S. 193-206

Mohr, Felix; Wever, Marcel

und Hüllermeier, Eyke

(2018): Reduction Stumps for Multi-class Classification. In: Duivesteijn, Wouter; Siebes, Arno und Ukkonen, Antti (Hrsg.): (2020): Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings. Lecture Notes in Computer Science, Bd. 11191. Cham: Springer. S. 225-237

Nguyen, Vu-Linh; Destercke, Sébastien und Hüllermeier, Eyke

(2019): Epistemic Uncertainty Sampling. In: Kralj Novak, Petra; Šmuc, Tomislav und Džeroski, Sašo (Hrsg.): Discovery Science. 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings. Lecture Notes in Computer Science, Bd. 11828. S. 72-86

Pfannschmidt, Karlson; Hüllermeier, Eyke

; Held, Susanne und Neiger, Reto (2016): Evaluating Tests in Medical Diagnosis: Combining Machine Learning with Game-Theoretical Concepts. In: Carvalho, Joao Paulo; Lesot, Marie-Jeanne; Kaymak, Uzay; Vieira, Susana; Bouchon-Meunier, Bernadette und Yager, Ronald R. (Hrsg.): Information Processing and Management of Uncertainty in Knowledge-Based Systems. 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part I. Communications in Computer and Information Science, Bd. 610. Cham: Springer. S. 450-461 [PDF, 519kB]

Rapp, Michael

; Mencía, Eneldo Loza; Fürnkranz, Johannes; Nguyen, Vu-Linh und Hüllermeier, Eyke

(Februar 2021): Learning Gradient Boosted Multi-label Classification Rules. In: Hutter, Frank; Kersting, Kristian; Lijffijt, Jefrey und Valera, Isabel (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III. Lecture Notes in Computer Science, Bd. 12459. Cham: Springer. S. 124-140 [PDF, 543kB]

Schäfer, Dirk und Hüllermeier, Eyke

(2015): Dyad Ranking Using A Bilinear Plackett-Luce Model. In: Appice, Annalisa; Pereira Rodrigues, Pedro; Santos Costa, Vítor; Gama, João; Jorge, Alípio und Soares, Carlos (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science, Bd. 9285. Cham: Springer. S. 227-242

Schäfer, Dirk und Hüllermeier, Eyke

(2018): Preference-Based Reinforcement Learning Using Dyad Ranking. In: Soldatova, Larisa; Vanschoren, Joaquin; Papadopoulos, George und Ceci, Michelangelo (Hrsg.): Discovery Science. Discovery Science 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings. Lecture Notes in Computer Science, Bd. 11198. Cham: Springer. S. 161-175

Senge, Robin; Del Coz, Juan José und Hüllermeier, Eyke

(2013): On the Problem of Error Propagation in Classifier Chains for Multi-label Classification. In: Spiliopoulou, Myra; Schmidt-Thieme, Lars und Janning, Ruth (Hrsg.): Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization, Cham: Springer. S. 163-170

Shabani, Aulon; Paul, Adil; Platon, Radu und Hüllermeier, Eyke

(2016): Predicting the Electricity Consumption of Buildings: An Improved CBR Approach. In: Goel, Ashok; Díaz-Agudo, M. Belén und Roth-Berghofer, Thomas (Hrsg.): Case-Based Reasoning Research and Development. Lecture Notes in Computer Science, Bd. 9969. Cham: Springer. S. 356-369

Shaker, Ammar; Heldt, Waleri und Hüllermeier, Eyke

(2017): Learning TSK Fuzzy Rules from Data Streams. In: Ceci, Michelangelo; Hollmén, Jaakko; Džeroski, Sašo; Todorovsk, Ljupčo; Vens, Celine und Džeroski, Sašo (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II. Lecture Notes in Computer Science, Bd. 10535. S. 559-574 [PDF, 351kB]

Shaker, Ammar und Hüllermeier, Eyke

(2013): Recovery Analysis for Adaptive Learning from Non-stationary Data Streams. In: Burduk, Robert; Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał und Zolnierek, Andrzej (Hrsg.): Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, Bd. 226. S. 289-298

Sulzmann, Jan-Nikolas; Fürnkranz, Johannes und Hüllermeier, Eyke

(2007): On Pairwise Naive Bayes Classifiers. In: Kok, Joost N.; Koronacki, Jacek; Lopez de Mantaras, Raomon; Matwin, Stan; Mladenič, Dunja und Skowron, Andrzej (Hrsg.): Machine Learning: ECML 2007. 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. Proceedings. Lecture Notes in Computer Science, Bd. 4701. Berlin, Heidelberg: Springer. S. 371-381

Tornede, Alexander

; Wever, Marcel

und Hüllermeier, Eyke

(Oktober 2020): Extreme Algorithm Selection with Dyadic Feature Representation. In: Appice, Annalisa; Tsoumakas, Grigorios; Manolopoulos, Yannis und Matwin, Stan (Hrsg.): (2021): Discovery Science. 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings. Lecture Notes in Computer Science, Bd. 12323. Cham: Springer. S. 309-324 [PDF, 213kB]

Vanderlooy, Stijn und Hüllermeier, Eyke

(2008): A Critical Analysis of Variants of the AUC. In: Daelemans, Walter; Goethals, Bart und Morik, Katharina (Hrsg.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 5211. Berlin, Heidelberg: Springer. S. 13

Wever, Marcel

; Tornede, Alexander

; Mohr, Felix und Hüllermeier, Eyke

(April 2020): LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. In: Berthold, Michael; Feelders, Ad und Krempl, Georg (Hrsg.): (2020): Advances in Intelligent Data Analysis XVIII. 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings. Lecture Notes in Computer Science, Bd. 12080. New York, NY: Association for Computing Machinery. S. 561-573 [PDF, 425kB]

Lexikon-Artikel

Fürnkranz, Johannes und Hüllermeier, Eyke

(2016): Preference Learning. In: Phung, Dinh; Webb, Geoffrey I. und Sammut, Claude (Hrsg.): Encyclopedia of Machine Learning and Data Mining. Living Edition. New York, NY: Springer. S. 1-7

Fürnkranz, Johannes und Hüllermeier, Eyke

(2011): Preference Learning. In: Sammut, Claude (Hrsg.): Encyclopedia of Machine Learning. 2010 Edition. Boston, MA: Springer. S. 789-795

Fürnkranz, Johannes und Hüllermeier, Eyke

(2017): Preference Learning. In: Sammut, Claude und Webb, Geoffrey I. (Hrsg.): Encyclopedia of Machine Learning and Data Mining. 2017 Edition. Boston, MA: Springer. S. 1000-1005

Fürnkranz, Johannes und Hüllermeier, Eyke

(2012): Preference Learning. In: Seel, N. M. (Hrsg.): Encyclopedia of the Sciences of Learning. 2012 Edition. Boston, MA: Springer

Konferenzbeitrag

Ahmadi Fahandar, Mohsen und Hüllermeier, Eyke (April 2021): Analogical Embedding for Analogy-Based Learning to Rank. Advances in Intelligent Data Analysis XIX. IDA 2021, Porto, Portugal, April 26-28, 2021. In: Advances in Intelligent Data Analysis XIX, Bd. 12695 Cham: Springer. S. 76-88

Ahmadi Fahandar, Mohsen und Hüllermeier, Eyke

(2018): Learning to rank based on analogical reasoning. 32nd AAAI Conference on Artificial Intelligence, New Orleans, Louisianna, USA, 2-7 February 2018. In: AAAI'18/IAAI'18/EAAI'18: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Proceedings of the Thirty-Second AAAI Conference on Artificial, AAAI Press: Palo Alto, California, USA. S. 2951-2958

Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke

und Couso, Inés (August 2017): Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. 34th International Conference on Machine Learning, Sydney, Australia. Precup, Doina und Teh, Yee Whye (Hrsg.): In: Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017, Bd. 70 [PDF, 269kB]

Bade, Korinna; Hüllermeier, Eyke

und Nurnberger, Andreas (2006): Hierarchical Classification by Expected Utility Maximization. 6th IEEE International Conference on Data Mining, Hong Kong, China, 18-22 December, 2006. Clifton, Christopher W. (Hrsg.): In: Sixth IEEE International Conference on Data Mining : ICDM 2006 ; 18-22 December 2006, Hong Kong, Los Alamitos, Calif.: IEEE Computer Society. S. 43-52

Belaid, Mohamed Karim; Bornemann, Richard; Rabus, Maximilian; Krestel, Ralf und Hüllermeier, Eyke

(Juli 2023): Compare-xAI: Toward Unifying Functional Testing Methods for Post-hoc XAI Algorithms into a Multi-dimensional Benchmark. World Conference on Explainable Artificial Intelligence (xAI 2023), Lisboa, Portugal, 26-28 July 2023. Longo, Luca (Hrsg.): Cham: Springer Nature Switzerland. S. 88-109 [PDF, 507kB]

Bengs, Viktor

; Haddenhorst, Björn und Hüllermeier, Eyke

(Mai 2024): Identifying Copeland Winners in Dueling Bandits with Indifferences. 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain, 2. - 4. May 2024. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research Bd. 238 PMLR. S. 226-234 [PDF, 522kB]

Bengs, Viktor und Hüllermeier, Eyke

(Juli 2020): Preselection Bandits. 37th International Conference on Machine Learning, Virtual, July 12-18 2020. III, Hal Daumé und Singh, Aarti (Hrsg.): In: Proceedings of the 37th International Conference on Machine Learning, Bd. 119 PMLR. S. 778-787 [PDF, 630kB]

Bengs, Viktor

; Hüllermeier, Eyke

und Waegeman, Willem

(28. November 2022): Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, 28 November - 9 December 2022. [PDF, 385kB]

Bengs, Viktor

; Hüllermeier, Eyke

und Waegeman, Willem

(2023): On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. 40th International Conference on Machine Learning (ICML 2023), Hawaii, USA, 23-29 July, 2023. Krause, Andreas; Brunskill, Emma; Cho, Kyunghyun; Engelhardt, Barbara; Sabato, Sivan und Scarlett, Jonathan (Hrsg.): In: Proceedings of the 40th International Conference on Machine Learning, Bd. 202 PMLR. S. 2078-2091 [PDF, 400kB]

Bengs, Viktor; Saha, Aadirupa und Hüllermeier, Eyke

(Juli 2022): Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. 39th International Conference on Machine Learning, Baltimore, MD, USA, July 17-23 2022. Chaudhuri, Kamalika; Jegelka, Stefanie; Song, Le; Szepesvari, Csaba; Niu, Gang und Sabato, Sivan (Hrsg.): In: Proceedings of the 39th International Conference on Machine Learning, Bd. 162 PMLR. S. 1764-1786 [PDF, 3MB]

Beringer, Jurgen und Hüllermeier, Eyke

(2007): Adaptive Optimization of the Number of Clusters in Fuzzy Clustering. 2007 IEEE International Fuzzy Systems Conference, London, UK, 23-26 July 2007. In: IEEE International Fuzzy Systems Conference, 2007: FUZZ-IEEE 2007 ; 23 - 26 July 2007, London, United Kingdom, Piscataway, NJ: IEEE. S. 1-6

Bernijazov, Ruslan; Dicks, Alexander; Dumitrescu, Roman; Foullois, Marc; Hanselle, Jonas

; Hüllermeier, Eyke

; Karakaya, Gökce; Ködding, Patrick; Lohweg, Volker; Malatyali, Manuel; Meyer Auf der Heide, Friedhelm; Panzner, Melina und Soltenborn, Christian (August 2021): A Meta-Review on Artificial Intelligence in Product Creation. AI and Product Design Workshop, 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Montreal, Quebec, Canada, 19. August 2021. [PDF, 1MB]

Bevanda, Petar; Beier, Max; Lederer, Armin; Sosnowski, Stefan; Hüllermeier, Eyke

und Hirche, Sandra (Dezember 2023): Koopman Kernel Regression. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, USA, 10. - 16. December 2023. In: Proceedings of the 37th Annual Conference on Neural Information Processing Systems, Advances in Neural Information Processing Systems Bd. 36 Curran Associates, Inc.. S. 16207-16221 [PDF, 466kB]

Boukhris, Imen; Elouedi, Zied; Fober, Thomas; Mernberger, Marco und Hüllermeier, Eyke

(2009): Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Detection. 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy, 30 Nov.-2 Dec. 2009. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications, Piscataway, NJ: IEEE. S. 1245-1250

Brandt, Jasmin; Bengs, Viktor

; Haddenhorst, Björn

und Hüllermeier, Eyke

(28. November 2022): Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, 28 November - 9 December 2022. [PDF, 618kB]

Brandt, Jasmin; Schede, Elias; Haddenhorst, Björn

; Bengs, Viktor

; Hüllermeier, Eyke

und Tierney, Kevin (7. Februar 2023): AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. AAAI Conference on Artificial Intelligence, Washington, DC, USA, February, 2023. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 37, Nr. 10 S. 12355-12363 [PDF, 844kB]

Brandt, Jasmin; Schede, Elias; Sharma, Shivam; Bengs, Viktor

; Hüllermeier, Eyke

und Tierney, Kevin (Oktober 2023): Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration. Lernen, Wissen, Daten, Analysen (LWDA), Marburg, Germany, 9-11 October 2023. Leyer, Michael und Wichmann, Johannes (Hrsg.): In: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Bd. 3630 CEUR-WS.org. S. 492-505 [PDF, 447kB]

Brandt, Jasmin; Wever, Marcel

; Bengs, Viktor

und Hüllermeier, Eyke

(August 2024): Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO. Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, South Korea, 3. - 9. August 2024. Larson, Kate (Hrsg.): In: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization. S. 3742-3750

Bresson, Roman; Cohen, Johanne; Hüllermeier, Eyke

; Labreuche, Christophe und Sebag, Michèle (Januar 2021): Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan (Virtual), January 7-15, 2021. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, S. 1984-1991

Bresson, Roman; Cohen, Johanne; Hüllermeier, Eyke

; Labreuche, Christophe und Sebag, Michèle (3. November 2021): On the Identifiability of Hierarchical Decision Models. International Conference on Principles of Knowledge Representation and Reasoning, Virtual, November 3-12, 2021. In: Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning, S. 151-162

Busa-Fekete, Róbert; Hüllermeier, Eyke

und Szörényi, Balázs (2014): Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows. 31st International Conference on Machine Learning (ICML 2014), Beijing, China, 21-26 June, 2014. Xing, Eric P. und Jebara, Tony (Hrsg.): In: Proceedings of the 31st International Conference on Machine Learning, Bd. 32, Nr. 2 Bejing, China: PMLR. S. 1071-1079 [PDF, 569kB]

Busa-Fekete, Róbert; Szörényi, Balázs; Cheng, Weiwei; Weng, Paul und Hüllermeier, Eyke

(2013): Top-k Selection based on Adaptive Sampling of Noisy Preferences. ICML'13: 30th International Conference on International Conference on Machine Learning, Atlanta GA USA, June 16 - 21, 2013. Dasgupta, Sanjoy und McAllester, David (Hrsg.): In: Proceedings of the 30th International Conference on International Conference on Machine Learning, Bd. 28, Nr. 3 S. 1094-1102 [PDF, 434kB]

Busa-Fekete, Róbert; Szörényi, Balázs und Hüllermeier, Eyke

(Juli 2014): PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. 28th AAAI Conference on Artificial Intelligence (AAAI-14), Quebec City, Canada, Jul 2014.

Campagner, Andrea

; Ciucci, Davide

und Hüllermeier, Eyke

(Juni 2020): Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Lisbon, Portugal, 15-19 June 2020. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Bd. 1237 Cham: Springer. S. 471-484 [PDF, 325kB]

Campagner, Andrea

; Lienen, Julian

; Hüllermeier, Eyke

und Ciucci, Davide

(2022): Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. International Joint Conference on Rough Sets, Suzhou, China, 11-14 November 2022. In: Rough Sets, Bd. 13633 Cham: Springer. S. 57-70

Caprio, Michele; Sale, Yusuf; Hüllermeier, Eyke

und Lee, Insup (4. August 2023): A Novel Bayes’ Theorem for Upper Probabilities. Epi UAI 2023 - First International Workshop on Epistemic Uncertainty in Artificial Intelligence, Pittsburgh, PA, USA, August 4, 2023. In: Epistemic Uncertainty in Artificial Intelligence, Bd. 14523 S. 1-12 [PDF, 171kB]

Cheng, Weiwei; Hüllermeier, Eyke

; Waegeman, Willem und Welker, Volkmar (2012): Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. 25. NIPS 2012, Lake Tahoe, Nevada, USA, December 3-8, 2012. Advances in Neural Information Processing Systems. Bd. 25 S. 2510-2518 [PDF, 252kB]

Cheng, Weiwei; Dembczyński, Krzysztof und Hüllermeier, Eyke

(2010): Graded multilabel classification: the ordinal case. ICML '10: 27th International Conference on Machine Learning, Haifa, Israel, June 21 - 24, 2010. In: ICML'10: Proceedings of the 27th International Conference on Machine Learning, Madison, WI: Omnipress. S. 223-230

Cheng, Weiwei; Henzgen, Sascha und Hüllermeier, Eyke

(2013): Labelwise versus Pairwise Decomposition in Label Ranking. LWA 2013. Lernen, Wissen & Adaptivität, Bamberg, Germany, 7.-9. October 2013. Henrich, Andreas und Sperker, Hans-Christian (Hrsg.): In: LWA 2013. Lernen, Wissen & Adaptivität, Workshop Proceedings Bamberg, 7.-9. October 2013, Dagstuhl Reports Bd. 4 Bamberg: Universitätsbibliothek Bamberg. S. 129-136

Cheng, Weiwei; Hühn, Jens und Hüllermeier, Eyke

(2009): Decision tree and instance-based learning for label ranking. ICML '09: 26th Annual International Conference on Machine Learning, Montreal Quebec, Canada, June 14 - 18, 2009. Danyluk, Andrea (Hrsg.): In: Proceedings of the 26th Annual International Conference on Machine Learning, New York, NY: Association for Computing Machinery. S. 161-168

Cheng, Weiwei und Hüllermeier, Eyke

(2013): A Nearest Neighbor Approach to Label Ranking based on Generalized Labelwise Loss Minimization. 7th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF '13), Beijing, China, August 3-5, 2013.

Cheng, Weiwei und Hüllermeier, Eyke

(2009): Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. LWA 2009: Workshop-Woche: Lernen, Wissen, Adaptivität, Darmstadt, Germany, 21.-23. September 2009. Hartmann, Melanie und Janssen, Frederik (Hrsg.): Darmstadt: FG Telekooperation/FG Knowledge Engineering, Technische Universität Darmstadt. S. 22-29

Czech, Mike; Hüllermeier, Eyke

; Jakobs, Marie-Christine und Wehrheim, Heike (2017): Predicting rankings of software verification tools. 3rd ACM SIGSOFT International Workshop on Software Analytics, Paderborn, Germany, 4 September 2017. Baysal, Olga (Hrsg.): In: Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics, ACM: New York, NY. S. 23-26

Damke, Clemens

und Hüllermeier, Eyke

(September 2024): CUQ-GNN: Committee-Based Graph Uncertainty Quantification Using Posterior Networks. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024), Vilnius, Lithuania, 9. - 13. September 2024. In: Machine Learning and Knowledge Discovery in Databases Part VIII, Proc. ECML PKDD 2024, LNCS Bd. 14948 Cham: Springer. S. 306-323 [PDF, 2MB]

Damke, Clemens

und Hüllermeier, Eyke

(11. Oktober 2021): Ranking Structured Objects with Graph Neural Networks. 24th International Conference on Discovery Science (DS 2021), Halifax, NS, Canada, Virtual, October 11–13, 2021. Soares, Carlos und Torgo, Luis (Hrsg.): In: Proceedings of the 24th International Conference on Discovery Science, Lecture Notes in Computer Science Bd. 12986 Cham: Springer. S. 166-180 [PDF, 649kB]

Damke, Clemens

; Melnikov, Vitalik und Hüllermeier, Eyke

(November 2020): A Novel Higher-order Weisfeiler-Lehman Graph Convolution. 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020. Jialin Pan, Sinno und Sugiyama, Masashi (Hrsg.): In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), Bd. 129 S. 49-64 [PDF, 771kB]

Dari, Simone und Hüllermeier, Eyke

(November 2020): Reliable Driver Gaze Classification based on Conformal Prediction. 30. Workshop Computational Intelligence, Berlin, Germany, 26. – 27. November 2020. Schulte, H.; Hoffmann, F. und Mikut, R. (Hrsg.): In: Proceedings. 30. Workshop Computational Intelligence Berlin, 26. – 27. November 2020, Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH) Bd. 29 Karlsruhe: KIT Scientific Publishing. S. 1-15 [PDF, 516kB]

Dari, Simone; Kadrileev, Nikolay und Hüllermeier, Eyke

(Juli 2020): A Neural Network-Based Driver Gaze Classification System with Vehicle Signals. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 19-24 July 2020. In: 2020 International Joint Conference on Neural Networks (IJCNN), S. 1-7

Dembczyński, Krzysztof; Jachnik, Arkadiusz; Kotlowski, Wojciech; Waegeman, Willem und Hüllermeier, Eyke

(2013): Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. ICML'13: 30th International Conference on International Conference on Machine Learning, Atlanta GA USA, June 16 - 21, 2013. Dasgupta, Sanjoy und McAllester, David (Hrsg.): In: Proceedings of the 30th International Conference on Machine Learning, Bd. 28, Nr. 3 S. 1030-1038 [PDF, 378kB]

Dembczyński, Krzysztof; Kotłowski, Wojciech und Hüllermeier, Eyke

(Juni 2012): Consistent multilabel ranking through univariate loss minimization. ICML'12: 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, 26 June 2012- 1 July 2012. Langford, John und Pineau, Joelle (Hrsg.): In: Proceedings of the Twenty-Ninth International Conference on Machine Learning, S. 1347-1354 [PDF, 425kB]

Drees, Jan Peter; Gupta, Pritha

; Hüllermeier, Eyke

; Jager, Tibor; Konze, Alexander; Priesterjahn, Claudia; Ramaswamy, Arunselvan und Somorovsky, Juraj (November 2021): Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, 15. November 2021. Carlini, Nicholas (Hrsg.): In: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, Bd. 22 New York: Association for Computing Machinery. S. 169-180

Dubois, Didier; Hüllermeier, Eyke

und Prade, Henri (Juli 2001): Toward the representation of implication-based fuzzy rules in terms of crisp rules. 20th NAFIPS International Conference, Vancouver, BC, Canada, 25-28 July 2001. Smith, Michael H. (Hrsg.): In: Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), Bd. 3 IEEE Operations Center: Piscataway, NJ. S. 1592-1597

El Mesaoudi-Paul, Adil; Hüllermeier, Eyke

und Busa-Fekete, Róbert (2018): Ranking Distributions based on Noisy Sorting. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Dy, Jennifer und Krause, Andreas (Hrsg.): In: Proceedings of the 35th International Conference on Machine Learning. ICML 2018, Bd. 80 S. 3472-3480 [PDF, 587kB]

Ewerth, Ralph; Springstein, Matthias; Müller, Eric; Balz, Alexander; Gehlhaar, Jan; Naziyok, Tolga; Dembczyński, Krzysztof und Hüllermeier, Eyke

(2017): Estimating relative depth in single images via rankboost. 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, 10-14 July 2017. In: 2017 IEEE International Conference on Multimedia and Expo (ICME) : Hong Kong, 10-14 July 2017, Piscataway, NJ: IEEE. S. 919-924

Fallah Tehrani, Ali; Strickert, Marc und Hüllermeier, Eyke

(2014): The Choquet kernel for monotone data. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium), 23-25 April 2014. Verleysen, Michel (Hrsg.): In: ESANN 2014 proceedings, Ciaco: Louvain-la-Neuve. S. 337-342

Fehring, Lukas

; Hanselle, Jonas

und Tornede, Alexander

(Dezember 2022): HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. Sixth Workshop on Meta-Learning (MetaLearn 2022) at the Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, U.S.A., 2. December 2022. [PDF, 356kB]

Feldhans, Robert; Wilke, Adrian; Heindorf, Stefan; Shaker, Mohammad Hossein; Hammer, Barbara; Ngonga Ngomo, Axel-Cyrille und Hüllermeier, Eyke

(November 2021): Drift Detection in Text Data with Document Embeddings. Intelligent Data Engineering and Automated Learning – IDEAL 2021, Manchester, United Kingdom, November 25-27, 2021. In: Intelligent Data Engineering and Automated Learning – IDEAL 2021, Bd. 13113 Cham: Springer. S. 107-118

Fischer, Raphael

; Wever, Marcel

; Buschjäger, Sebastian

und Liebig, Thomas

(September 2024): MetaQuRe: Meta-learning from Model Quality and Resource Consumption. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024), Vilnius, Lithuania, 9. - 13. September 2024. In: Machine Learning and Knowledge Discovery in Databases Part VII, Proc. ECML PKDD 2024, Bd. 14947, Nr. 14947 Springer Cham. S. 209-226

Fober, Thomas und Hüllermeier, Eyke

(2010): Similarity measures for protein structures based on fuzzy histogram comparison. International Conference on Fuzzy Systems, Barcelona, Spain, 18-23 July 2010. In: International Conference on Fuzzy Systems, Piscataway, NJ: IEEE. S. 1-7

Fober, Thomas; Klebe, Gerhard und Hüllermeier, Eyke

(2009): Efficient Construction of Multiple Geometrical Alignments for the Comparison of Protein Binding Sites. 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy, 30 Nov.-2 Dec. 2009. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications, Piscataway, NJ: IEEE. S. 1251-1256

Fober, Thomas; Mernberger, Marco; Melnikov, Vitalik; Moritz, Ralph und Hüllermeier, Eyke

(2009): Extension and Empirical Comparison of Graph-Kernels for the Analysis of Protein Active Sites. LWA 2009: Workshop-Woche: Lernen, Wissen, Adaptivität, Darmstadt, Germany, 21.-23. September 2009. Hartmann, Melanie und Janssen, Frederik (Hrsg.): Darmstadt: FG Telekooperation/FG Knowledge Engineering, Technische Universität Darmstadt. S. 30-36

Fumagalli, Fabian

; Muschalik, Maximilian

; Hüllermeier, Eyke

und Hammer, Barbara

(4. Oktober 2023): On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 - European Symposium on Artificial Neural Networks, Bruges, Belgium, 4-6 October 2023. In: Proceedings of ESANN 2023, S. 83-88 [PDF, 1MB]

Fumagalli, Fabian; Muschalik, Maximilian

; Kolpaczki, Patrick; HÜllermeier, Eyke

und Hammer, Barbara

(Juli 2024): KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024. In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research Bd. 235 PMLR. S. 14308-14342 [PDF, 1MB]

Fumagalli, Fabian

; Muschalik, Maximilian

; Kolpaczki, Patrick; Hüllermeier, Eyke

und Hammer, Barbara

(Dezember 2023): SHAP-IQ: Unified Approximation of any-order Shapley Interactions. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, USA, 10. - 16. December 2023. In: Proceedings of the 37th Annual Conference on Neural Information Processing Systems, Advances in Neural Information Processing Systems Bd. 36 Curran Associates, Inc.. S. 11515-11551 [PDF, 1MB]

Giovagnoli, Alessandro; Tresp, Volker; Ma, Yunpu und Schubert, Matthias (2023): QNEAT: Natural Evolution of Variational Quantum Circuit Architecture. GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon Portugal, July 15 - 19, 2023. Silva, Sara und Paquete, Luís (Hrsg.): In: GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, New York: Association for Computing Machinery. S. 647-650

Gupta, Pritha

; Drees, Jan Peter

und Hüllermeier, Eyke

(2023): Automated Side-Channel Attacks using Black-Box Neural Architecture Search. 18th International Conference on Availability, Reliability and Security, Benevento, Italy, 29 August - 1 September 2023. In: Proceedings of the 18th International Conference on Availability, Reliability and Security, New York, NY, USA: Association for Computing Machinery. S. 1-11 [PDF, 969kB]

Gupta, Pritha

; Ramaswamy, Arunselvan

; Drees, Jan Peter

; Hüllermeier, Eyke

; Priesterjahn, Claudia

und Jager, Tibor

(Februar 2022): Automated Information Leakage Detection: A New Method Combining Machine Learning and Hypothesis Testing with an Application to Side-channel Detection in Cryptographic Protocols. 14th International Conference on Agents and Artificial Intelligence (ICAART 2022), Virtual, February 3-5. In: Proceedings of the 14th International Conference on Agents and Artificial Intelligence, Bd. 2 S. 152-163 [PDF, 592kB]

Haas, Stefan

und Hüllermeier, Eyke

(September 2022): A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain. Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France, 19-23 September, 2023. In: Machine Learning and Knowledge Discovery in Databases, Bd. 13718 Cham: Springer. S. 170-184

Haas, Stefan

und Hüllermeier, Eyke

(18. September 2023): Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Torino, Italy, 18-22 September, 2023. In: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, Bd. 14174 S. 3-18

Haddenhorst, Björn

; Bengs, Viktor; Brandt, Jasmin und Hüllermeier, Eyke

(27. Juli 2021): Testification of Condorcet Winners in dueling bandits. Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, Virtual, July 27-30, 2021. de Campos, Cassio und Maathuis, Marloes H. (Hrsg.): In: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, Bd. 161 PMLR. S. 1195-1205 [PDF, 875kB]

Haddenhorst, Björn

; Bengs, Viktor und Hüllermeier, Eyke

(7. Dezember 2021): Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. Advances in Neural Information Processing Systems, Virtual, December 7 2021. Ranzato, M.; Beygelzimer, A.; Dauphin, Y.; Liang, P.S. und Vaughan, J. Wortman (Hrsg.): Bd. 34 Curran Associates, Inc.. S. 25904-25916 [PDF, 516kB]

Hanselle, Jonas; Fürnkranz, Johannes und Hüllermeier, Eyke (2023): Probabilistic Scoring Lists for Interpretable Machine Learning. 26th International Conference on Discovery Science (DS 2023), Porto, Portugal, October 9-11, 2023. In: Discovery Science, Bd. 14276 Cham: Springer. S. 189-203 [PDF, 1MB]

Hanselle, Jonas

; Kornowicz, Jaroslaw; Heid, Stefan; Thommes, Kirsten

und Hüllermeier, Eyke

(Oktober 2023): Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. Lernen, Wissen, Daten, Analysen (LWDA), Marburg, Germany, 9-11 October 2023. Leyer, Michael und Wichmann, Johannes (Hrsg.): In: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Bd. 3630 CEUR-WS.org. S. 430-441 [PDF, 953kB]

Hanselle, Jonas; Tornede, Alexander

; Wever, Marcel

und Hüllermeier, Eyke

(September 2020): Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020. Schmid, Ute; Klügl, Franziska und Wolter, Diedrich (Hrsg.): In: KI 2020: Advances in Artificial Intelligence, Proceedings, Lecture Notes in Computer Science Bd. 12325 Cham: Springer. S. 59-72 [PDF, 377kB]

Heid, Stefan; Kornowicz, Jaroslaw; Hüllermeier, Eyke

und Thommes, Kirsten

(November 2024): Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems. 34. Workshop Computational Intelligence, Berlin, Germany, 21.-22. November 2024. Schulte, Horst; Hoffmann, Frank und Mikut, Ralf (Hrsg.): KIT Scientific Publishing. S. 233-252 [PDF, 448kB]

Heid, Stefan; Ramaswamy, Arunselvan und Hüllermeier, Eyke

(November 2020): Constrained Multi-Agent Optimization with Unbounded Information Delay. 30. Workshop Computational Intelligence, Berlin, Germany, 26. – 27. November 2020. Schulte, H.; Hoffmann, F. und Mikut, R. (Hrsg.): In: Proceedings. 30. Workshop Computational Intelligence Berlin, 26. – 27. November 2020, Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH) Bd. 29 Karlsruhe: KIT Scientific Publishing. S. 247-261 [PDF, 369kB]

Herrmann, Moritz

; Lange, F. Julian D.; Eggensperger, Katharina; Casalicchio, Giuseppe; Wever, Marcel

; Feurer, Matthias; Rügamer, David

; Hüllermeier, Eyke

; Boulesteix, Anne-Laure und Bischl, Bernd (Juli 2024): Position: Why We Must Rethink Empirical Research in Machine Learning. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024. In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research Bd. 235 PMLR. S. 18228-18247 [PDF, 334kB]

Hetzer, Alexander; Wever, Marcel

; Mohr, Felix und Hüllermeier, Eyke

(2019): Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. ECDA 2019: European Conference on Data Analysis, Bayreuth, Germany, 18 - 20 March 2019. In: ECDA 2019: Book of Abstracts, S. 65-66

Hoffmann, Frank und Hüllermeier, Eyke

(Dezember 2009): Multiple Geometrical Alignments for the Analysis of Protein Active Sites. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009. Hoffmann, Frank und Hüllermeier, Eyke (Hrsg.): In: Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009, Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH) Bd. 29 Karlsruhe: KIT Scientific Publishing. S. 237-255 [PDF, 453kB]

Hüllermeier, Eyke

(September 2020): Towards Analogy-Based Explanations in Machine Learning. Modeling Decisions for Artificial Intelligence, Sant Cugat, Spain, September 2–4, 2020. In: Modeling Decisions for Artificial Intelligence, Bd. 12256 Cham: Springer. S. 205-217 [PDF, 859kB]

Hüllermeier, Eyke

; Destercke, Sébastien und Shaker, Mohammad Hossein (August 2022): Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison. 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 1-5 August 2022. Cussens, James und Zhang, Kun (Hrsg.): In: Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, Bd. 180 PMLR. S. 548-557 [PDF, 426kB]

Hüllermeier, Eyke

; Fürnkranz, Johannes und Loza Mencia, Eneldo (2020): Conformal Rule-Based Multi-label Classification. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020. In: KI 2020: Advances in Artificial Intelligence, Bd. 12325 Cham: Springer. S. 290-296 [PDF, 350kB]

Hüllermeier, Eyke

; Mohr, Felix; Tornede, Alexander

und Wever, Marcel

(September 2021): Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECMLPKDD Workshop on Automating Data Science (ADS2021), Virtual, 17 September 2021. [PDF, 136kB]

Hüllermeier, Eyke

und Fürnkranz, J. (2004): Ranking by pairwise comparison a note on risk minimization. 2004 IEEE International Conference on Fuzzy Systems, Budapest, Hungary, 25-29 July, 2004. In: 2004 IEEE International Conference on Fuzzy Systems : proceedings : Budapest, Hungary, 25-29 July, 2004, Bd. 1 Piscataway, N.J: IEEE. S. 97-102

Hüllermeier, Eyke

; Fürnkranz, Johannes; Loza Mencia, Eneldo; Nguyen, Vu-Linh

und Rapp, Michael

(August 2020): Rule-Based Multi-label Classification: Challenges and Opportunities. 4th International Joint Conference on Rules and Reasoning, Virtual, 29 June - 1 July 2020. In: Rules and Reasoning, Bd. 12173 Cham: Springer. S. 3-19

Hüllermeier, Eyke

; Weskamp, Nils; Klebe, Gerhard und Kuhn, Daniel (2007): Graph Alignment: Fuzzy Pattern Mining for the Structural Analysis of Protein Active Sites. 2007 IEEE International Fuzzy Systems Conference, London, UK, 23-26 July 2007. In: 2007 IEEE International Fuzzy Systems Conference, Piscataway, NJ: IEEE. S. 1-6

Jasinska, Kalina; Dembczynski, Krzysztof; Busa-Fekete, Róbert; Pfannschmidt, Karlson; Klerx, Timo und Hüllermeier, Eyke

(2016): Extreme F-measure maximization using sparse probability estimates. ICML'16: 33rd International Conference on International Conference on Machine Learning, New York, NY USA, June 19 - 24, 2016. Balcan, Maria Florina und Weinberger, Kilian Q. (Hrsg.): In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, Bd. 22 New York: Association for Computing Machinery. S. 1435-1444 [PDF, 719kB]

Javanmardi, Alireza; Sale, Yusuf; Hofman, Paul und Hüllermeier, Eyke

(September 2023): Conformal Prediction with Partially Labeled Data. Twelfth Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023), Limassol, Cyprus, September 13-15, 2023. Papadopoulos, Harris; Nguyen, Khuong An; Boström, Henrik und Carlsson, Lars (Hrsg.): Bd. 204 PMLR. S. 251-266 [PDF, 343kB]

Javanmardi, Alireza; Stutz, David und Hüllermeier, Eyke

(Dezember 2024): Conformalized Credal Set Predictors. The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, BC, Canada, 10. -15. December 2024. [PDF, 3MB]

Juergens, Mira; Meinert, Nis; Bengs, Viktor

; Hüllermeier, Eyke

und Waegeman, Willem (Juli 2024): Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024. In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research Bd. 235 PMLR. S. 22624-22642 [PDF, 8MB]

Kaufmann, Timo

; Ball, Sarah; Beck, Jakob; Hüllermeier, Eyke

und Kreuter, Frauke (22. September 2023): On the challenges and practices of reinforcement learning from real human feedback. First ECMLPKDD Workshop on Hybrid Human-Machine Learning and Decision Making, Turin, Italy, 22. September 2023. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer Cham. [Forthcoming] [PDF, 1MB]

Kaufmann, Timo

; Bengs, Viktor

und Hüllermeier, Eyke

(2024): Reinforcement Learning from Human Feedback for Cyber-Physical Systems: On the Potential of Self-Supervised Pretraining. International Conference on Machine Learning For Cyber-Physical Systems (ML4CPS 2023), Hamburg, Germany, 29. - 31. March 2023. Niggemann, Oliver; Beyerer, Jürgen; Krantz, Maria und Kühnert, Christian (Hrsg.): In: Technologien für die intelligente Automation, Bd. 18 Cham: Springer Nature Switzerland. S. 11-18 [PDF, 380kB]

Kaufmann, Timo

; Blüml, Jannis; Wüst, Antonia; Delfosse, Quentin; Kersting, Kristian und Hüllermeier, Eyke

(August 2024): OCALM: Object-Centric Assessment with Language Models. Reinforcement Learning Conference (RLC 2024) Workshop on Reinforcement Learning Beyond Rewards (RLBRew), Amherst, MA, USA, 9. August 2024.

Kolpaczki, Patrick; Bengs, Viktor und Hüllermeier, Eyke

(September 2021): Identifying Top-k Players in Cooperative Games via Shapley Bandits. LWDA’21: Lernen, Wissen, Daten, Analysen, Munich, Germany, 1. - 3. September, 2021. In: Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Bd. 2993 S. 133-144 [PDF, 805kB]

Kolpaczki, Patrick; Bengs, Viktor

; Muschalik, Maximilian

und Hüllermeier, Eyke

(2024): Approximating the Shapley Value without Marginal Contributions. AAAI Conference on Artificial Intelligence 2024, Vancouver, Canada, 20-27 February 2024. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 38, Nr. 12 S. 13246-13255 [PDF, 2MB]

Kolpaczki, Patrick; Haselbeck, Georg und Hüllermeier, Eyke

(Juli 2024): How Much Can Stratification Improve the Approximation of Shapley Values? Explainable Artificial Intelligence, Valletta, Malta, 17. –19. July 2024. Longo, Luca; Lapuschkin, Sebastian und Seifert, Christin (Hrsg.): In: Communications in Computer and Information Science, Bd. 2154 Springer, Cham. S. 489-512

Kolpaczki, Patrick; Muschalik, Maximilian

; Fumagalli, Fabian

; Hammer, Barbara

und Hüllermeier, Eyke

(Mai 2024): SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain, 2. - 4. May 2024. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research Bd. 238 PMLR. S. 3520-3528 [PDF, 8MB]

Leinweber, Matthias; Baumgärtner, Lars; Mernberger, Marco; Fober, Thomas; Hüllermeier, Eyke

; Klebe, Gerhard und Freisleben, Bernd (2012): GPU-based Cloud computing for comparing the structure of protein binding sites. 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST), Campione d'Italia, Italy, 18-20 June 2012. In: Proceedings of the 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST) - Complex Environment Engineering Municipal Casino, Campione d’Italia (Italy) 18 – 20 June 2012, Piscataway, NJ: IEEE. S. 1-6

Lienen, Julian

; Demir, Caglar und Hüllermeier, Eyke

(September 2023): Conformal Credal Self-Supervised Learning. Twelfth Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023), Limassol, Cyprus, September 13-15, 2023. Papadopoulos, Harris; Nguyen, Khuong An; Boström, Henrik und Carlsson, Lars (Hrsg.): Bd. 204 PMLR. S. 214-233 [PDF, 539kB]

Lienen, Julian

und Hüllermeier, Eyke

(Februar 2024): Mitigating Label Noise through Data Ambiguation. AAAI Conference on Artificial Intelligence 2024, Vancouver, Canada, 20-27 February 2024. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 38, Nr. 12 S. 13799-13807 [Video (MP4), 775MB]

Lienen, Julian

und Hüllermeier, Eyke

(Dezember 2021): Credal Self-Supervised Learning. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Virtual, December 7, 2021. Ranzato, M.; Beygelzimer, A.; Dauphin, Y.; Liang, P.S. und Vaughan, J. Wortman (Hrsg.): Advances in Neural Information Processing Systems. Bd. 34 Curran Associates, Inc.. S. 14370-14382 [PDF, 405kB]

Lienen, Julian

; Nommensen, Nils; Ewerth, Ralph und Hüllermeier, Eyke

(17. November 2021): Robust Regression for Monocular Depth Estimation. Asian Conference on Machine Learning, Virtual, November 17 - 19, 2021. Balasubramanian, Vineeth N. und Tsang, Ivor (Hrsg.): In: Proceedings of The 13th Asian Conference on Machine Learning, Bd. 157 PMLR. S. 1001-1016 [PDF, 451kB]

Lienen, Julian und Hüllermeier, Eyke

(Februar 2021): From Label Smoothing to Label Relaxation. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Virtual, February 2–9, 2021. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 35, Nr. 10 S. 8583-8591

Lienen, Julian; Hüllermeier, Eyke

; Ewerth, Ralph und Nommensen, Nils (Juni 2021): Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20-25 June 2021. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ: IEEE. S. 14590-14599

Lughofer, Edwin und Hüllermeier, Eyke

(2011): On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure. EUSFLAT - LFA 2011, Aix-Les-Bains, France, 18 - 22 July 2011. In: Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), Atlantis Press. S. 380-387 [PDF, 2MB]

Löhr, Timo

; Ingrisch, Michael

und Hüllermeier, Eyke

(Juli 2024): Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification. 22nd International Conference of Artificial Intelligence in Medicine (AIME 2024), Salt Lake City, Utah, USA, 9. - 12. July 2024. In: Artificial Intelligence in Medicine, LNAI Bd. 14845 Springer Cham. S. 145-155

Melnikov, Vitalik und Hüllermeier, Eyke

(2019): Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. 11th Asian Conference on Machine Learning (ACML 2019), Nagoya, Japan, November 17 - 19, 2019. Wee Sun, Lee und Suzuki, Taiji (Hrsg.): In: Proceedings of The Eleventh Asian Conference on Machine Learning, Bd. 101 S. 1110-1125 [PDF, 2MB]

Mohr, Felix; Hüllermeier, Eyke

und Wever, Marcel

(Juli 2018): ML-Plan for Unlimited-Length Machine Learning Pipelines. ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018-07-10 – 2018-07-15. [PDF, 297kB]

Mohr, Felix und Wever, Marcel

(Juli 2021): Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual, 2021-07-23. [PDF, 381kB]

Mohr, Felix

; Bengs, Viktor und Hüllermeier, Eyke

(Februar 2021): Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. AAAI Conference on Artificial Intelligence, Virtual, February 2–9, 2021. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 35, Nr. 14 S. 12373-12381

Mohr, Felix; Lettmann, Theodor; Hüllermeier, Eyke

und Wever, Marcel Dominik

(Juni 2018): Programmatic Task Network Planning. 28th International Conference on Automated Planning and Scheduling, Delft, Netherlands, 2018-06-24 – 2018-06-29. Bercher, Pascal; Höller, Daniel; Biundo, Susanne und Alford, Ron (Hrsg.): In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, S. 31-39

Mohr, Felix; Wever, Marcel

und Hüllermeier, Eyke

(2018): On-the-Fly Service Construction with Prototypes. 2018 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA, 2-7 July 2018. In: 2018 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE. S. 225-232

Mohr, Felix; Wever, Marcel

; Hüllermeier, Eyke

und Faez, Amin (2018): (WIP) Towards the Automated Composition of Machine Learning Services. 2018 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA, 2-7 July 2018. In: 2018 IEEE International Conference on Services Computing : IEEE SCC 2018 : part of the 2018 IEEE World Congress on Services : 2-7 July 2018, San Francisco, California, USA : proceedings, Piscataway, NJ: IEEE. S. 241-244

Mortier, Thomas

; Bengs, Viktor

; Hüllermeier, Eyke

; Luca, Stijn und Waegeman, Willem

(April 2023): On the Calibration of Probabilistic Classifier Sets. 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Valencia, Spain, 25-27 April, 2023. Ruiz, Francisco; Dy, Jennifer und van de Meent, Jan-Willem (Hrsg.): In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, Bd. 206 PMLR. S. 8857-8870 [PDF, 764kB]

Mortier, Thomas

; Hüllermeier, Eyke

; Dembczyński, Krzysztof und Waegeman, Willem

(August 2022): Set-valued prediction in hierarchical classification with constrained representation complexity. 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 1-5 August 2022. Cussens, James und Zhang, Kun (Hrsg.): In: Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, Bd. 180 PMLR. S. 1392-1401 [PDF, 600kB]

Muschalik, Maximilian

; Fumagalli, Fabian

; Hammer, Barbara

und Hüllermeier, Eyke

(Februar 2024): Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. AAAI Conference on Artificial Intelligence 2024, Vancouver, Canada, 20-27 February 2024. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 38, Nr. 13 S. 14388-14396 [PDF, 3MB]

Muschalik, Maximilian

; Fumagalli, Fabian

; Hammer, Barbara

und Hüllermeier, Eyke

(September 2024): Explaining Change in Models and Data with Global Feature Importance and Effects. TempXAI: Explainable AI for Time Series and Data Streams Tutorial-Workshop, Vilnius, Lithuania, 9. - 13. September 2024. [PDF, 243kB]

Muschalik, Maximilian

; Fumagalli, Fabian

; Hammer, Barbara

und Hüllermeier, Eyke

(18. September 2023): iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Torino, Italy, 18-22 September, 2023. In: Machine Learning and Knowledge Discovery in Databases: Research Track, Bd. 14171 Cham: Springer. S. 428-445 [PDF, 3MB]

Muschalik, Maximilian

; Fumagalli, Fabian

; Jagtani, Rohit; Hammer, Barbara

und Hüllermeier, Eyke

(Juli 2023): iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. World Conference on Explainable Artificial Intelligence (xAI 2023), Lisboa, Portugal, 26-28 July 2023. Longo, Luca (Hrsg.): Cham: Springer Nature Switzerland. S. 177-194

Nasiri, Maryam; Fober, Thomas; Senge, Robin und Hüllermeier, Eyke

(2013): Fuzzy pattern trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Edmonton, AB, Canada, 24-28 June 2013. In: Proceedings of the 2013 Joint IFSA World Congress NAFIPS Annual Meeting (IFSAI NAFIPS), Edmonton, Canada, June 24-28, 2013, Piscataway, NJ: IEEE. S. 715-721

Nguyen, Duc Anh; Levie, Ron; Lienen, Julian

; Hüllermeier, Eyke

und Kutyniok, Gitta (Mai 2023): Memorization-Dilation: Modeling Neural Collapse Under Noise. The Eleventh International Conference on Learning Representations, Kigali, Rwanda, 1-5 May, 2023. [PDF, 510kB]

Nguyen, Vu-Linh; Destercke, Sébastien; Masson, Marie-Hélène und Hüllermeier, Eyke

(2018): Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 13-19 July 2018. Lang, Jérôme (Hrsg.): In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Menlo Park, County of San Mateo, California: S. 5089-5095

Nguyen, Vu-Linh

; Hüllermeier, Eyke

; Rapp, Michael

; Loza Mencia, Eneldo und Fürnkranz, Johannes (15. Oktober 2020): On Aggregation in Ensembles of Multilabel Classifiers. 23rd International Conference on Discovery Science, Virtual, 19-21 October 2020. In: Discovery Science, Bd. 12323 Cham: Springer. S. 533-547 [PDF, 354kB]

Nguyen, Vu-Linh

und Hüllermeier, Eyke

(Februar 2020): Reliable Multilabel Classification: Prediction with Partial Abstention. 34th AAAI Conference on Artificial Intelligence, New York, USA, 7-12 Feburary 2020. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 34, Nr. 04 Palo Alto, California USA: AAAI Press. S. 5264-5271

Paul, Adil und Hüllermeier, Eyke

(2015): A CBR Approach to the Angry Birds Game. Twenty-Third International Conference on Case-Based Reasoning (ICCBR 2015), Frankfurt, Germany, September 28-30, 2015. Kendall-Morwick, Joseph (Hrsg.): In: Workshop Proceedings from The Twenty-Third International Conference on Case-Based Reasoning (ICCBR 2015), S. 68-77

Peeters, Sven; Melnikov, Vitalik und Hüllermeier, Eyke

(26. April 2021): Performance Prediction for Hardware-Software Configurations: A Case Study for Video Games. 19th International Symposium on Intelligent Data Analysis, Porto, Portugal, April 26–28, 2021. In: Advances in Intelligent Data Analysis XIX, Bd. 12695 S. 222-234

Pfannschmidt, Karlson

und Hüllermeier, Eyke

(2020): Learning Choice Functions via Pareto-Embeddings. 43rd German Conference on AI, Bamberg, Germany, 21-25 September, 2020. In: KI 2020: Advances in Artificial Intelligence, Bd. 12325 Cham: Springer. S. 327-333 [PDF, 494kB]

Rahnama, Javad und Hüllermeier, Eyke

(5. Juni 2020): Learning Tversky Similarity. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Lisbon, Portugal, 15-19 June 2020. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Bd. 1238 Cham: Springer. S. 269-280 [PDF, 418kB]

Rapp, Michael

; Mencía, Eneldo Loza; Fürnkranz, Johannes und Hüllermeier, Eyke

(September 2021): Gradient-Based Label Binning in Multi-label Classification. Machine Learning and Knowledge Discovery in Databases, Bilbao, Spain, September 13–17, 2021. In: Machine Learning and Knowledge Discovery in Databases. Research Track, Bd. 12977 Cham: Springer. S. 462-477 [PDF, 471kB]

Rodemann, Julian

; Kreiss, Dominik; Hüllermeier, Eyke

und Augustin, Thomas (Oktober 2022): Levelwise Data Disambiguation by Cautious Superset Classification. 15th International Conference on Scalable Uncertainty Management, Paris, France, 17-19 October 2022. In: Proceedings of the 15th International Conference on Scalable Uncertainty Management, Bd. 13562 Cham: Springer. S. 263-276

Sale, Yusuf; Bengs, Viktor

; Caprio, Michele und Hüllermeier, Eyke

(Juli 2024): Second-Order Uncertainty Quantification: A Distance-Based Approach. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024. In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research Bd. 235 PMLR. S. 43060-43076 [PDF, 2MB]

Sale, Yusuf; Caprio, Michele und Hüllermeier, Eyke

(August 2023): Is the volume of a credal set a good measure for epistemic uncertainty? Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, PA, USA, 1-3 August, 2023. Evans, Robin J. und Shpitser, Ilya (Hrsg.): In: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, Bd. 216 PMLR. S. 1795-1804 [PDF, 1MB]

Schede, Elias; Brandt, Jasmin; Tornede, Alexander

; Wever, Marcel

; Bengs, Viktor

; Hüllermeier, Eyke

und Tierney, Kevin (19. August 2023): A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, S.A.R, 19-25 August 2023. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, S. 6964-6968

Schneider, Stefanie

; Springstein, Matthias; Rahnama, Javad; Hüllermeier, Eyke

; Ewerth, Ralph

und Kohle, Hubertus

(2021): The Dissimilar in the Similar. An Attribute-guided Approach to the Subject-specific Classification of Art-historical Objects. INFORMATIK 2020 - Back to the Future, Virtual, 28 September - 2 October 2020. Reussner, Ralf H.; Koziolek, Anne und Heinrich, Robert (Hrsg.): In: INFORMATIK 2020, 1355-1364 Bonn: Gesellschaft für Informatik. [PDF, 1MB]

Schneider, Stefanie

; Springstein, Matthias; Rahnama, Javad; Kohle, Hubertus

; Ewerth, Ralph

und Hüllermeier, Eyke

(März 2022): iART - Eine Suchmaschine zur Unterstützung von bildorientierten Forschungsprozessen. DHd 2022 Kulturen des digitalen Gedächtnisses. 8. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum", Potsdam, 7. - 11. März 2022. [PDF, 18MB]

Schubert, David; Gupta, Pritha

und Wever, Marcel

(April 2023): Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets. Advances in Intelligent Data Analysis XXI, Louvain-la-Neuve, Belgium, 12-14 April 2023. Crémilleux, Bruno; Hess, Sibylle und Nijssen, Siegfried (Hrsg.): Cham: Springer Nature Switzerland. S. 392-405 [PDF, 599kB]

Schäfer, Dirk und Hüllermeier, Eyke

(2016): Plackett-Luce Networks for Dyad Ranking. LWDA 2016: Lernen, Wissen, Daten, Analysen 2016, Potsdam, Germany, September 12-14, 2016. Krestel, Ralf; Mottin, Davide und Müller, Emmanuel (Hrsg.): In: Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", Bd. 22 S. 323-334 [PDF, 461kB]

Schäfer, Dirk und Hüllermeier, Eyke

(2015): Preference-based meta-learning using dyad ranking: recommending algorithms in cold-start situations. MetaSel'15: International Conference on Meta-Learning and Algorithm Selection, Porto Portugal, 7 September 2015. Vanschoren, Joaquin; Brazdil, Pavel und Giraud-Carrier, Christophe (Hrsg.): In: MetaSel'15: Proceedings of the 2015 International Conference on Meta-Learning and Algorithm Selection, Bd. 1455 CEUR-WS.org. S. 110-111

Seemann, Nina; Merten, Marie-Luis; Geierhos, Michaela; Tophinke, Doris und Hüllermeier, Eyke

(August 2017): Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Vancouver, Canada, August, 2017. In: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Vancouver, Canada: Association for Computational Linguistics. S. 40-45 [PDF, 125kB]

Seemann, Nina; Geierhos, Michaela; Merten, Marie-Luis; Tophinke, Doris; Wever, Marcel Dominik

und Hüllermeier, Eyke

(März 2018): Supporting the Cognitive Process in Annotation Tasks. 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, Stuttgart, Germany, 2018-03-07 – 2018-03-09.

Senge, Robin und Hüllermeier, Eyke

(2010): Pattern trees for regression and fuzzy systems modeling. 2010 IEEE International Conference on Fuzzy Systems (FUZZ‐IEEE 2010), Barcelona, Spain, 18-23 July 2010. In: International Conference on Fuzzy Systems (FUZZ 2010) : Barcelona, Spain, 18 - 23 July 2010, Piscataway, NJ: IEEE. S. 1-7

Senge, Robin und Hüllermeier, Eyke

(2009): Learning Pattern Tree Classifiers Using a Co-Evolutionary Algorithm. LWA 2009: Workshop-Woche: Lernen, Wissen, Adaptivität, Darmstadt, Germany, 21.-23. September 2009. Hartmann, Melanie und Janssen, Frederik (Hrsg.): Darmstadt: FG Telekooperation/FG Knowledge Engineering, Technische Universität Darmstadt. S. 105-110

Shaker, Mohammad Hossein und Hüllermeier, Eyke

(April 2020): Aleatoric and Epistemic Uncertainty with Random Forests. International Symposium on Intelligent Data Analysis, Bodenseeforum, Lake Constance, Germany, April 27-29 2020. In: Advances in Intelligent Data Analysis XVIII, Bd. 12080 Cham: Springer. S. 444-456 [PDF, 1MB]

Shaker, Ammar und Hüllermeier, Eyke

(2013): Event history analysis on data streams: An application to earthquake occurence. RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic, September 27, 2013. Krempl, Georg; Zliobaite, Indre; Wang, Yin und Forman, Georg (Hrsg.): In: Real-World Challenges for Data Stream Mining : Proceedings of the 1st International Workshop on Real-World Challenges for Data Stream Mining, ReaiStream 2013, Magdeburg: Universität Magdeburg. S. 38-41

Sharma, Arnab; Melnikov, Vitalik; Hüllermeier, Eyke

und Wehrheim, Heike (Februar 2023): Property-Driven Black-Box Testing of Numeric Functions. Software Engineering 2023, Paderborn, Germany, 20-24 February 2023. Engels, Gregor; Hebig, Regina und Tichy, Matthias (Hrsg.): 111-112 Bonn: Gesellschaft für Informatik e.V.. S. 111-112 [PDF, 213kB]

Sharma, Arnab; Melnikov, Vitalik; Hüllermeier, Eyke

und Wehrheim, Heike (Mai 2022): Property-Driven Testing of Black-Box Functions. 2022 IEEE/ACM 10th International Conference on Formal Methods in Software Engineering (FormaliSE), Pittsburgh, PA, USA, 22-23 May 2022. S. 113-123

Springstein, Matthias; Schneider, Stefanie

; Rahnama, Javad; Hüllermeier, Eyke

; Kohle, Hubertus

und Ewerth, Ralph

(Oktober 2021): iART: A Search Engine for Art-Historical Images to Support Research in the Humanities. 29th ACM International Conference on Multimedia, Virtual, October 20 - 24, 2021. In: Proceedings of the 29th ACM International Conference on Multimedia, S. 2801-2803 [PDF, 14MB]

Szarvas, György; Busa-Fekete, Róbert und Hüllermeier, Eyke

(2013): Learning to Rank Lexical Substitutions. 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, 18-21 October 2013. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing,, Association for Computational Linguistics. S. 1926-1932 [PDF, 139kB]

Szörényi, Baiÿzs; Busa-Fekete, Róbert; Paul, Adil und Hüllermeier, Eyke

(2015): Online F-measure optimization. NIPS'15: 28th International Conference on Neural Information Processing Systems, Montreal Canada, December 7 - 12, 2015. Cortes, C.; Lee, D. D.; Sugiyama, M. und Garnett, R. (Hrsg.): In: NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, Bd. 1 Cambridge, MA: MIT Press. S. 595-603 [PDF, 585kB]

Szörényi, Baiÿzs; Busa-Fekete, Róbert; Paul, Adil und Hüllermeier, Eyke

(2015): Online rank elicitation for Plackett-Luce: a dueling bandits approach. NIPS'15: 28th International Conference on Neural Information Processing Systems, Montreal Canada, December 7 - 12, 2015. Cortes, C.; Lee, D. D.; Sugiyama, M. und Garnett, R. (Hrsg.): In: NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, Bd. 1 Cambridge, MA: MIT Press. S. 604-612 [PDF, 534kB]

Szörényi, Balázs; Busa-Fekete, Róbert; Weng, Paul und Hüllermeier, Eyke

(Juli 2015): Qualitative Multi-Armed Bandits: A Quantile-Based Approach. 32nd International Conference on Machine Learning, Lille, France, July 6 - 11, 2015. In: Proceedings of the 32nd International Conference on Machine Learning, Bd. 37 S. 1660-1668 [PDF, 449kB]

Tehrani, Ali Fallah; Cheng, Weiwei und Hüllermeier, Eyke

(2011): Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral. EUSFLAT - LFA 2011, Aix-Les-Bains, France, 18 - 22 July 2011. In: Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), Advances in Intelligent Systems Research S. 868-875 [PDF, 1MB]

Tehrani, Ali Fallah und Hüllermeier, Eyke

(2013): Ordinal Choquistic Regression. 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2013), Milan, Italy, 11 - 13 September 2013. In: 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2013) : Milan, Italy, 11 - 13 September 2013, Advances in intelligent systems research Bd. 32 Red Hook, NY: Curran. S. 802-809 [PDF, 1MB]

Tornede, Alexander

; Wever, Marcel

und Hüllermeier, Eyke

(Dezember 2020): Towards Meta-Algorithm Selection. 4th Workshop on Meta-Learning at NeurIPS, Virtual, 11 December 2020. arxiv.org. [PDF, 313kB]

Tornede, Alexander

; Wever, Marcel

und Hüllermeier, Eyke

(November 2019): Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. , KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146. 29. Workshop Computational Intelligence, Dortmund, Germany, 28. - 29. November 2019. Hoffmann, Frank; Hüllermeier, Eyke und Mikut, Ralf (Hrsg.): In: Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, Karlsruhe: KIT Scientific Publishing. S. 135-146 [PDF, 812kB]

Tornede, Alexander

; Wever, Marcel

; Werner, Stefan; Mohr, Felix und Hüllermeier, Eyke

(November 2020): Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020. Jialin Pan, Sinno und Sugiyama, Masashi (Hrsg.): In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), Bd. 129 S. 737-752 [PDF, 390kB]

Tornede, Alexander

; Bengs, Viktor und Hüllermeier, Eyke

(Juni 2022): Machine Learning for Online Algorithm Selection under Censored Feedback. Thirty-Sixth AAAI Conference on Artificial Intelligence, Virtual, February 22–March 1, 2022. Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 36, Nr. 9 S. 10370-10380

Tornede, Tanja

; Tornede, Alexander

; Wever, Marcel

und Hüllermeier, Eyke

(Juli 2021): Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. GECCO '21, Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lille, France, 10 - 14 July, 2021. Chicano, Francisco (Hrsg.): In: Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY: Association for Computing Machinery. S. 368-376

Tornede, Tanja; Tornede, Alexander

; Wever, Marcel

; Mohr, Felix

und Hüllermeier, Eyke

(September 2020): AutoML for Predictive Maintenance: One Tool to RUL Them All. Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020. Gama, Joao (Hrsg.): In: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, Septem, Bd. 1325 Cham: Springer. S. 106-118

Vahidi, Amihossein; Wimmer, Lisa; Gündüz, Hüseyin Anil; Bischl, Bernd

; Hüllermeier, Eyke

und Rezaei, Mina (September 2024): Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024), Vilnius, Lithuania, 9. - 13. September 2024. In: Machine Learning and Knowledge Discovery in Databases Part I, LNCS Bd. 14941 Springer Cham. S. 38-55 [PDF, 904kB]

Vahidi, Amirhossein; Schosser, Simon; Wimmer, Lisa; Li, Yawei; Bischl, Bernd

; Hüllermeier, Eyke

und Rezaei, Mina (Mai 2024): Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization. 12th International Conference on Learning Representations (ICLR 2024), Vienna, Austria, 7. - 11. May 2024. [PDF, 439kB]

Weng, Paul; Busa-Fekete, Róbert und Hüllermeier, Eyke

(2013): Interactive Q-Learning with Ordinal Rewards and Unreliable Tutor. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013). Reinforcement Learning with Generalized Feedback, Prague, 23rd September 2013. S. 1-13

Weng, Paul; Busa-Fekete, Róbert und Hüllermeier, Eyke

(2013): Preference-based Evolutionary Direct Policy Search. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013). Reinforcement Learning with Generalized Feedback, Prague, 23rd September 2013. S. 1-8

Wever, Marcel

; Özdogan, Miran und Hüllermeier, Eyke

(2023): Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. Genetic and Evolutionary Computation Conference (GECCO '23), Lisbon, Portugal, July 15-19, 2023. In: Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA: Association for Computing Machinery. 597–605

Wever, Marcel Dominik

; Mohr, Felix und Hüllermeier, Eyke

(2017): Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. 27th Workshop Computational Intelligence, Dortmund, 23. – 24. November 2017. Hoffmann, F.; Hüllermeier, Eyke und Mikut, R. (Hrsg.): In: Proceedings. 27. Workshop Computational Intelligence Dortmund, 23. – 24. November 2017, Karlsruhe: KIT Scientific Publishing. S. 149-166 [PDF, 657kB]

Wever, Marcel Dominik

; Mohr, Felix; Tornede, Alexander

und Hüllermeier, Eyke

(2019): Automating Multi-Label Classification Extending ML-Plan. 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, California, USA, June 14th 2019. [PDF, 388kB]

Wever, Marcel

; Mohr, Felix und Hüllermeier, Eyke

(2018): Ensembles of evolved nested dichotomies for classification. GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, July 15 - 19, 2018. In: Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY: Association for Computing Machinery. S. 561-568

Wever, Marcel

; Mohr, Felix; Hüllermeier, Eyke

und Hetzer, Alexander (2019): Towards Automated Machine Learning for Multi-Label Classification. ECDA 2019: European Conference on Data Analysis, Bayreuth, Germany, 18 - 20 March 2019. In: ECDA 2019: Book of Abstracts, S. 65

Wever, Marcel

; van Rooijen, Lorijn und Hamann, Heiko (2017): Active coevolutionary learning of requirements specifications from examples. GECCO '17: Genetic and Evolutionary Computation Conference, Berlin, Germany, July 15 - 19, 2017. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference July 2017, New York, NY: Association for Computing Machinery. S. 1327-1334

Wickert, Anna-Katharina

; Damke, Clemens

; Baumgärtner, Lars

; Hüllermeier, Eyke

und Mezini, Mira

(15. Mai 2023): UnGoML: Automated Classification of unsafe Usages in Go. 20th International Conference on Mining Software Repositories (MSR), Melbourne, Australia, 15-16 May, 2023. In: 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR), IEEE. S. 309-321 [PDF, 2MB]

Wimmer, Lisa; Sale, Yusuf; Hofman, Paul; Bischl, Bernd

und Hüllermeier, Eyke

(2023): Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, PA, USA, 1-3 August, 2023. Evans, Robin J. und Shpitser, Ilya (Hrsg.): In: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, Bd. 216 PMLR. S. 2282-2292 [PDF, 1MB]

Zhang, Yao; Ma, Yunpu

; Seidl, Thomas und Tresp, Volker

(2023): Adaptive Multi-Resolution Attention with Linear Complexity. 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 18-23 June 2023. In: IJCNN 2023, International Joint Conference on Neural Networks, 18-23 June 2023, Gold Coast Convention and Exhibition Centre, Queensland, Australia, Piscataway, NJ, USA: IEEE.

de Calmes, Martine; Dubois, Didier; Hüllermeier, Eyke

; Prade, Henri und Sedes, Florence (2002): Case-based querying and prediction: a fuzzy set approach. 2002 IEEE World Congress on Computational Intelligence, Honolulu, HI, USA, 12-17 May 2002. In: 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings. Volume 2, Bd. 1 Piscataway, NJ: IEEE. S. 735-739

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