N. Epple, S. Dari, L. Drees, V. Protschky, and A. Riener, “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries,” in 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.
LibreCat: /record/15009
DOI: 10.1109/ivs.2019.8814100

A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019, pp. 135–146.
LibreCat: /record/15011

K. Brinker and E. Hüllermeier, “A Reduction of Label Ranking to Multiclass Classification,” in Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2019.
LibreCat: /record/15013

E. Hüllermeier, I. Couso, and S. Diestercke, “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants,” in Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.
LibreCat: /record/15014

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

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

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

F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.
LibreCat: /record/13132

M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.
LibreCat: /record/10232

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

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