F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018.
LibreCat: /record/2479
DOI: 10.1109/SCC.2018.00039

K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” arXiv:1803.05796. 2018.
LibreCat: /record/19524

F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, Delft, Netherlands, 2018, pp. 31–39.
LibreCat: /record/2857

A. Ramaswamy and S. Bhatnagar, “Stability of stochastic approximations with ‘controlled markov’ noise and temporal difference learning,” IEEE Transactions on Automatic Control, vol. 64, no. 6, pp. 2614–2620, 2018.
LibreCat: /record/24150

B. Demirel, A. Ramaswamy, D. E. Quevedo, and H. Karl, “Deepcas: A deep reinforcement learning algorithm for control-aware scheduling,” IEEE Control Systems Letters, vol. 2, no. 4, pp. 737–742, 2018.
LibreCat: /record/24151

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

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

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

F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class Classification,” in Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands.
LibreCat: /record/3552
DOI: 10.1007/978-3-030-01768-2_19

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

M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018.
LibreCat: /record/2109
DOI: 10.1145/3205455.3205562

M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.
LibreCat: /record/17713

F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.
LibreCat: /record/17714

H. Graf, Ranking of Classification Algorithms in AutoML. Universität Paderborn, 2018.
LibreCat: /record/5693

M. Scheibl, Learning about learning curves from dataset properties. Universität Paderborn, 2018.
LibreCat: /record/5936

D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.
LibreCat: /record/6423
DOI: 10.1007/978-3-030-01771-2_11

S. Abiteboul et al., Eds., Research Directions for Principles of Data Management, vol. 7, no. 1. 2018, pp. 1–29.
LibreCat: /record/10591

I. Couso and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in Frontiers in Computational Intelligence, S. Mostaghim, A. Nürnberger, and C. Borgelt, Eds. Springer, 2018, pp. 31–46.
LibreCat: /record/10783

D. Schäfer and E. Hüllermeier, “Dyad ranking using Plackett-Luce models based on joint feature representations,” Machine Learning, vol. 107, no. 5, pp. 903–941, 2018.
LibreCat: /record/16038

M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank Based on Analogical Reasoning,” in Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958.
LibreCat: /record/10145