Ranking Structured Objects with Graph Neural Networks
C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021), Springer, 2021, pp. 166–180.
LibreCat: /record/27381
DOI: 10.1007/978-3-030-88942-5
arXiv: 2104.08869

Automated Machine Learning for Multi-Label Classification
M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021.
LibreCat: /record/27284
DOI: 10.17619/UNIPB/1-1302

Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data
J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).
LibreCat: /record/21198

Learning Choice Functions via Pareto-Embeddings
K. Pfannschmidt, E. Hüllermeier, in: Lecture Notes in Computer Science, Cham, 2020.
LibreCat: /record/19521
DOI: 10.1007/978-3-030-58285-2_30

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.
LibreCat: /record/19953
arXiv: 2007.00346

Preselection Bandits
V. Bengs, E. Hüllermeier, in: International Conference on Machine Learning, 2020, pp. 778–787.
LibreCat: /record/21534

Multi-Armed Bandits with Censored Consumption of Resources
V. Bengs, E. Hüllermeier, ArXiv:2011.00813 (2020).
LibreCat: /record/21536

Extreme Algorithm Selection with Dyadic Feature Representation
A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.
LibreCat: /record/17407

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.
LibreCat: /record/17408

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.
LibreCat: /record/17424
DOI: 10.1007/978-3-030-66770-2_8

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.).
LibreCat: /record/17605

Towards Meta-Algorithm Selection
A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
LibreCat: /record/20306

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.
LibreCat: /record/18014
DOI: 10.1007/978-3-030-53552-0_22

Online Preselection with Context Information under the Plackett-Luce  Model
A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).
LibreCat: /record/18017

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.
LibreCat: /record/18276

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.).
LibreCat: /record/16725

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.
LibreCat: /record/15629

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.
LibreCat: /record/15025
DOI: 10.1162/evco_a_00266

Learning Choice Functions: Concepts and Architectures
K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019).
LibreCat: /record/19523

Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff
M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146.
LibreCat: /record/17565

Uniform approximation in classical weak convergence theory
V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).
LibreCat: /record/18018

Towards Automated Machine Learning for Multi-Label Classification
M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.
LibreCat: /record/8868

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.
LibreCat: /record/10578

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.
LibreCat: /record/15001
DOI: 10.1109/mci.2018.2881642

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.
LibreCat: /record/15002
DOI: 10.1007/s10618-018-0595-5

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.
LibreCat: /record/15003

Feature Selection for Analogy-Based Learning to Rank
M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.
LibreCat: /record/15004
DOI: 10.1007/978-3-030-33778-0_22

Analogy-Based Preference Learning with Kernels
M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial Intelligence, Cham, 2019.
LibreCat: /record/15005
DOI: 10.1007/978-3-030-30179-8_3

Epistemic Uncertainty Sampling
V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham, 2019.
LibreCat: /record/15006
DOI: 10.1007/978-3-030-33778-0_7

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.
LibreCat: /record/15007
DOI: 10.1016/j.jmva.2019.02.017

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.
LibreCat: /record/15009
DOI: 10.1109/ivs.2019.8814100

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.
LibreCat: /record/15011

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.
LibreCat: /record/15013

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.
LibreCat: /record/15014

Mining Rank Data
S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from Data (2019) 1–36.
LibreCat: /record/15015
DOI: 10.1145/3363572

Asymptotic confidence sets for the jump curve in bivariate regression problems
V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312.
LibreCat: /record/14027
DOI: 10.1016/j.jmva.2019.02.017

Adaptive confidence sets for kink estimation
V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.
LibreCat: /record/14028
DOI: 10.1214/19-ejs1555

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.
LibreCat: /record/13132

Automating Multi-Label Classification Extending ML-Plan
M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.
LibreCat: /record/10232

Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches
K. Rohlfing, G. Leonardi, I. Nomikou, J. Rczaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).
LibreCat: /record/20243
DOI: 10.1109/TCDS.2019.2892991

(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.
LibreCat: /record/2479
DOI: 10.1109/SCC.2018.00039

Deep Architectures for Learning Context-dependent Ranking Functions
K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).
LibreCat: /record/19524

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.
LibreCat: /record/2857

Stability of stochastic approximations with ccontrolled markovd noise and temporal difference learning
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.
LibreCat: /record/24150

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.
LibreCat: /record/24151

On-The-Fly Service Construction with Prototypes
F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.
LibreCat: /record/2471
DOI: 10.1109/SCC.2018.00036

On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis
V. Melnikov, E. Hüllermeier, Machine Learning (2018).
LibreCat: /record/3402
DOI: 10.1007/s10994-018-5733-1

ML-Plan: Automated Machine Learning via Hierarchical Planning
F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
LibreCat: /record/3510
DOI: 10.1007/s10994-018-5735-z

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.
LibreCat: /record/3552
DOI: 10.1007/978-3-030-01768-2_19

ML-Plan for Unlimited-Length Machine Learning Pipelines
M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.
LibreCat: /record/3852