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Christian Wirth
Christian Wirth
AI Engineer, Continental Automotive GmbH
Verified email at christianwirth.net
Title
Cited by
Cited by
Year
A survey of preference-based reinforcement learning methods
C Wirth, R Akrour, G Neumann, J Fürnkranz
Journal of Machine Learning Research 18 (136), 1-46, 2017
4012017
UBY-a large-scale unified lexical-semantic resource based on LMF
I Gurevych, J Eckle-Kohler, S Hartmann, M Matuschek, CM Meyer, C Wirth
Proceedings of the 13th Conference of the European Chapter of the …, 2012
1742012
Model-free preference-based reinforcement learning
C Wirth, J Fürnkranz, G Neumann
Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016
1152016
On learning from game annotations
C Wirth, J Fürnkranz
IEEE Transactions on Computational Intelligence and AI in Games 7 (3), 304-316, 2014
252014
Preference-based reinforcement learning: A preliminary survey
C Wirth, J Fürnkranz
Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning from …, 2013
252013
Informed hybrid game tree search for general video game playing
T Joppen, MU Moneke, N Schröder, C Wirth, J Fürnkranz
IEEE Transactions on Games 10 (1), 78-90, 2017
232017
EPMC: Every visit preference Monte Carlo for reinforcement learning
C Wirth, J Fürnkranz
Asian Conference on Machine Learning, 483-497, 2013
172013
Knowledge augmented machine learning with applications in autonomous driving: A survey
J Wörmann, D Bogdoll, C Brunner, E Bührle, H Chen, EF Chuo, ...
arXiv preprint arXiv:2205.04712, 2022
152022
A policy iteration algorithm for learning from preference-based feedback
C Wirth, J Fürnkranz
International Symposium on Intelligent Data Analysis, 427-437, 2013
122013
Preference-based Monte Carlo tree search
T Joppen, C Wirth, J Fürnkranz
KI 2018: Advances in Artificial Intelligence: 41st German Conference on AI …, 2018
92018
First steps towards learning from game annotations
C Wirth, J Fürnkranz
Proceedings of the ECAI Workshop on Preference Learning: Problems and …, 2012
72012
Efficient Preference-based Reinforcement Learning
C Wirth
Technische Universität, 2017
42017
Post-hoc rule based explanations for black box bayesian optimization
T Chakraborty, C Wirth, C Seifert
European Conference on Artificial Intelligence, 320-337, 2023
32023
Overcoming the limitations of localization uncertainty: Efficient and exact non-linear post-processing and calibration
M Kassem Sbeyti, M Karg, C Wirth, A Nowzad, S Albayrak
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023
32023
Concept embeddings for fuzzy logic verification of deep neural networks in perception tasks
G Schwalbe, C Wirth, U Schmid
arXiv preprint arXiv:2201.00572, 2022
32022
Humanzentrierte Künstliche Intelligenz: Erklärendes interaktives maschinelles Lernen für Effizienzsteigerung von Parametrieraufgaben
C Wirth, U Schmid, S Voget
Digitalisierung souverän gestalten II: Handlungsspielräume in digitalen …, 2022
32022
Informed Priors for Knowledge Integration in Trajectory Prediction
C Schlauch, C Wirth, N Klein
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023
22023
Efficient utility function learning for multi-objective parameter optimization with prior knowledge
FA Khan, JP Dietrich, C Wirth
arXiv preprint arXiv:2208.10300, 2022
22022
Design of a smart helmet
L Hottner, E Bachlmair, M Zeppetzauer, C Wirth, A Ferscha
Proceedings of the Seventh International Conference on the Internet of …, 2017
22017
Informed Hybrid Game Tree Search
T Joppen, M Moneke, N Schröder, C Wirth, J Fümkranz
Knowledge Engineering Group, Technische Universität Darmstadt, Tech. Rep., 2016
12016
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