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449 Publications


2020 | Preprint | LibreCat-ID: 17605 | OA
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.).
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2020 | Conference Paper | LibreCat-ID: 20306
Towards Meta-Algorithm Selection
A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
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2020 | Book Chapter | LibreCat-ID: 18014
Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach
A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in: Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232.
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2020 | Preprint | LibreCat-ID: 18017
Online Preselection with Context Information under the Plackett-Luce Model
A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).
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2020 | Conference Paper | LibreCat-ID: 18276
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.
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2020 | Journal Article | LibreCat-ID: 16725
Algorithm Selection for Software Validation Based on Graph Kernels
C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.).
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2020 | Conference Paper | LibreCat-ID: 15629
LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification
M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.
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2020 | Journal Article | LibreCat-ID: 15025
Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets
M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193.
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2019 | Preprint | LibreCat-ID: 19523
Learning Choice Functions: Concepts and Architectures
K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019).
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2019 | Journal Article | LibreCat-ID: 17565
Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff
M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146.
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2019 | Preprint | LibreCat-ID: 18018
Uniform approximation in classical weak convergence theory
V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).
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2019 | Conference Abstract | LibreCat-ID: 8868
Towards Automated Machine Learning for Multi-Label Classification
M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.
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2019 | Journal Article | LibreCat-ID: 10578
Choice Functions Generated by Mallows and Plackett–Luce Relations
V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213.
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2019 | Journal Article | LibreCat-ID: 15001
Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning
I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence Magazine (2019) 31–44.
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2019 | Journal Article | LibreCat-ID: 15002 | OA
Multi-target prediction: a unifying view on problems and methods
W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324.
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2019 | Conference Paper | LibreCat-ID: 15003
Set-Valued Prediction in Multi-Class Classification
T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.
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2019 | Book Chapter | LibreCat-ID: 15004
Feature Selection for Analogy-Based Learning to Rank
M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.
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2019 | Book Chapter | LibreCat-ID: 15005
Analogy-Based Preference Learning with Kernels
M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial Intelligence, Cham, 2019.
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2019 | Book Chapter | LibreCat-ID: 15006
Epistemic Uncertainty Sampling
V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham, 2019.
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2019 | Conference Paper | LibreCat-ID: 15007 | OA
Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA
V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.
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2019 | Conference Paper | LibreCat-ID: 15009
Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries
N. Epple, S. Dari, L. Drees, V. Protschky, A. Riener, in: 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.
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2019 | Conference Paper | LibreCat-ID: 15011 | OA
Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking
A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.
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2019 | Conference Paper | LibreCat-ID: 15013
A Reduction of Label Ranking to Multiclass Classification
K. Brinker, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Würzburg, Germany, 2019.
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2019 | Conference Paper | LibreCat-ID: 15014
Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants
E. Hüllermeier, I. Couso, S. Diestercke, in: Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.
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2019 | Journal Article | LibreCat-ID: 15015
Mining Rank Data
S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from Data (2019) 1–36.
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2019 | Journal Article | LibreCat-ID: 14027
Asymptotic confidence sets for the jump curve in bivariate regression problems
V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312.
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2019 | Journal Article | LibreCat-ID: 14028
Adaptive confidence sets for kink estimation
V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.
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2019 | Conference Abstract | LibreCat-ID: 13132
From Automated to On-The-Fly Machine Learning
F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274.
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2019 | Conference Paper | LibreCat-ID: 10232 | OA
Automating Multi-Label Classification Extending ML-Plan
M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.
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2019 | Journal Article | LibreCat-ID: 20243
Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches
K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).
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2018 | Conference Paper | LibreCat-ID: 2479 | OA
(WIP) Towards the Automated Composition of Machine Learning Services
F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018.
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2018 | Preprint | LibreCat-ID: 19524
Deep Architectures for Learning Context-dependent Ranking Functions
K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).
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2018 | Conference Paper | LibreCat-ID: 2857 | OA
Programmatic Task Network Planning
F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.
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2018 | Journal Article | LibreCat-ID: 24150
Stability of stochastic approximations with “controlled markov” noise and temporal difference learning
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.
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2018 | Journal Article | LibreCat-ID: 24151
Deepcas: A deep reinforcement learning algorithm for control-aware scheduling
B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters 2 (2018) 737–742.
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2018 | Conference Paper | LibreCat-ID: 2471 | OA
On-The-Fly Service Construction with Prototypes
F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.
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2018 | Journal Article | LibreCat-ID: 3402 LibreCat | Files available | DOI
 

2018 | Journal Article | LibreCat-ID: 3510 | OA
ML-Plan: Automated Machine Learning via Hierarchical Planning
F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
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2018 | Conference Paper | LibreCat-ID: 3552 | OA
Reduction Stumps for Multi-Class Classification
F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.
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2018 | Conference Paper | LibreCat-ID: 3852 | OA
ML-Plan for Unlimited-Length Machine Learning Pipelines
M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.
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2018 | Conference Paper | LibreCat-ID: 2109 | OA
Ensembles of Evolved Nested Dichotomies for Classification
M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.
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2018 | Preprint | LibreCat-ID: 17713 | OA
Automated Multi-Label Classification based on ML-Plan
M.D. Wever, F. Mohr, E. Hüllermeier, (2018).
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2018 | Preprint | LibreCat-ID: 17714 | OA
Automated machine learning service composition
F. Mohr, M.D. Wever, E. Hüllermeier, (2018).
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2018 | Bachelorsthesis | LibreCat-ID: 5693
Ranking of Classification Algorithms in AutoML
H. Graf, Ranking of Classification Algorithms in AutoML, Universität Paderborn, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Learning about learning curves from dataset properties
M. Scheibl, Learning about Learning Curves from Dataset Properties, Universität Paderborn, 2018.
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2018 | Book Chapter | LibreCat-ID: 6423
Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International Publishing, Cham, 2018, pp. 161–175.
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2018 | Conference (Editor) | LibreCat-ID: 10591
Research Directions for Principles of Data Management
S. Abiteboul, M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, E. Hüllermeier, B. Kimelfeld, L. Libkin, W. Martens, T. Milo, F. Murlak, F. Neven, M. Ortiz, T. Schwentick, J. Stoyanovich, J. Su, D. Suciu, V. Vianu, K. Yi, eds., Research Directions for Principles of Data Management, 2018.
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2018 | Book Chapter | LibreCat-ID: 10783
Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, E. Hüllermeier, in: S. Mostaghim, A. Nürnberger, C. Borgelt (Eds.), Frontiers in Computational Intelligence, Springer, 2018, pp. 31–46.
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2018 | Journal Article | LibreCat-ID: 16038
Dyad ranking using Plackett-Luce models based on joint feature representations
D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.
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2018 | Conference Paper | LibreCat-ID: 10145
Learning to Rank Based on Analogical Reasoning
M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958.
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