Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce  model
J. Lienen, E. Hüllermeier, ArXiv:2010.13118 (2020).

Constrained Multi-Agent Optimization with Unbounded Information Delay
S.H. Heid, A. Ramaswamy, E. Hüllermeier, in: Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020, 2020, p. 247.

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

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.

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.

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.).

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

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.

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

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.

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.).

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.

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

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.

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.

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.

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.

Feature Selection for Analogy-Based Learning to Rank
M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.

Analogy-Based Preference Learning with Kernels
M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial Intelligence, Cham, 2019.

Epistemic Uncertainty Sampling
V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham, 2019.