Christian Wirth
Christian Wirth
AI Engineer, Continental Automotive GmbH
Verified email at christianwirth.net
Title
Cited by
Cited by
Year
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
1592012
A survey of preference-based reinforcement learning methods
C Wirth, R Akrour, G Neumann, J Fürnkranz
The Journal of Machine Learning Research 18 (1), 4945-4990, 2017
722017
Model-free preference-based reinforcement learning
C Wirth, J Fürnkranz, G Neumann
Thirtieth AAAI Conference on Artificial Intelligence, 2016
492016
EPMC: Every visit preference Monte Carlo for reinforcement learning
C Wirth, J Fürnkranz
Asian Conference on Machine Learning, 483-497, 2013
152013
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
142017
Preference-based reinforcement learning: A preliminary survey
C Wirth, J Fürnkranz
Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning from …, 2013
142013
On learning from game annotations
C Wirth, J Fürnkranz
IEEE Transactions on Computational Intelligence and AI in Games 7 (3), 304-316, 2014
102014
A policy iteration algorithm for learning from preference-based feedback
C Wirth, J Fürnkranz
International Symposium on Intelligent Data Analysis, 427-437, 2013
62013
First steps towards learning from game annotations
C Wirth, J Fürnkranz
52012
Preference-Based Monte Carlo Tree Search
T Joppen, C Wirth, J Fürnkranz
Joint German/Austrian Conference on Artificial Intelligence (Künstliche …, 2018
32018
Efficient Preference-based Reinforcement Learning
C Wirth
Technische Universität, 2017
22017
Informed Hybrid Game Tree Search
T Joppen, M Moneke, N Schröder, C Wirth, J Fürnkranz
Knowledge Engineering Group, Technische Universität Darmstadt, Tech. Rep., 2016
12016
Learning from trajectory-based action preferences
C Wirth, J Fürnkranz
Proceedings of the ICRA 2013 Workshop on Autonomous Learning (to appear, 2013
12013
Efficient Preference-based Reinforcement Learning: Using Surrogates for Solving Markov Decision Processes with Preferences
C Wirth
Technische Universität Darmstadt, 2017
2017
Preference Learning from Annotated Game Databases.
C Wirth, J Fürnkranz
LWA, 57-68, 2014
2014
Stroke-und Trimap-basierte Mattingverfahren
A Eigenstetter, C Wirth
2008
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Articles 1–16