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431 Publications


2021 | Journal Article | LibreCat-ID: 21004
AutoML for Multi-Label Classification: Overview and Empirical Evaluation
M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.
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2021 | Journal Article | LibreCat-ID: 21092
Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning
F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.).
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2021 | Conference Paper | LibreCat-ID: 21198
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data
J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: 2021.
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2020 | Conference Paper | LibreCat-ID: 19953
A Novel Higher-order Weisfeiler-Lehman Graph Convolution
C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR, Bangkok, Thailand, 2020, pp. 49–64.
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2020 | Conference Paper | LibreCat-ID: 17407
Extreme Algorithm Selection with Dyadic Feature Representation
A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.
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2020 | Conference Paper | LibreCat-ID: 17408
Hybrid Ranking and Regression for Algorithm Selection
J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 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: 17605
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: 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 | 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: 19521
Learning Choice Functions via Pareto-Embeddings
K. Pfannschmidt, E. Hüllermeier, in: Lecture Notes in Computer Science, Cham, 2020.
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2020 | Conference Paper | LibreCat-ID: 17424
AutoML for Predictive Maintenance: One Tool to RUL Them All
T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 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 | 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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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 | Journal Article | LibreCat-ID: 15002
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: 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 | Conference Paper | LibreCat-ID: 15007
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 | 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 | 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 | 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 | 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 | Conference Paper | LibreCat-ID: 10232
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: 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 | 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 | 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
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 | 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 | Preprint | LibreCat-ID: 18016
Preselection Bandits under the Plackett-Luce Model
V. Bengs, E. Hüllermeier, ArXiv:1907.06123 (n.d.).
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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 | 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: 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 | 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: 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 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: 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 (n.d.).
LibreCat | Files available | DOI
 

2018 | Conference Paper | LibreCat-ID: 10184
Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175.
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2018 | Conference Paper | LibreCat-ID: 2479
(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 | Conference Paper | LibreCat-ID: 3852
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 (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 | Preprint | LibreCat-ID: 17713
Automated Multi-Label Classification based on ML-Plan
M.D. Wever, F. Mohr, E. Hüllermeier, (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 | 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 | Conference Paper | LibreCat-ID: 10148
Ranking Distributions based on Noisy Sorting
A. El Mesaoudi-Paul, E. Hüllermeier, R. Busa-Fekete, in: Proc. 35th Int. Conference on Machine Learning (ICML), 2018, pp. 3469–3477.
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2018 | Conference Paper | LibreCat-ID: 10181
Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty
V.-L. Nguyen, S. Destercke, M.-H. Masson, E. Hüllermeier, in: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095.
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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: 2109
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 | Conference Paper | LibreCat-ID: 2471
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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