Marc Toussaint
Marc Toussaint
Professor of Computer Science, University of Stuttgart, Germany
Verified email at - Homepage
TitleCited byYear
Using machine learning to focus iterative optimization
F Agakov, E Bonilla, J Cavazos, B Franke, G Fursin, MFP O'Boyle, ...
Proceedings of the international symposium on code generation and …, 2006
Robot trajectory optimization using approximate inference
M Toussaint
Proceedings of the 26th annual international conference on machine learning …, 2009
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
M Toussaint, A Storkey
Proceedings of the 23rd international conference on Machine learning, 945-952, 2006
On stochastic optimal control and reinforcement learning by approximate inference
K Rawlik, M Toussaint, S Vijayakumar
Twenty-Third International Joint Conference on Artificial Intelligence, 2013
A no-free-lunch theorem for non-uniform distributions of target functions
C Igel, M Toussaint
Journal of Mathematical Modelling and Algorithms 3 (4), 313-322, 2005
On classes of functions for which no free lunch results hold
C Igel, M Toussaint
arXiv preprint cs/0108011, 2001
Probabilistic inference as a model of planned behavior.
M Toussaint
KI 23 (3), 23-29, 2009
Gaussian process implicit surfaces for shape estimation and grasping
S Dragiev, M Toussaint, M Gienger
2011 IEEE International Conference on Robotics and Automation, 2845-2850, 2011
Planning as inference
M Botvinick, M Toussaint
Trends in cognitive sciences 16 (10), 485-488, 2012
Multi-class image segmentation using conditional random fields and global classification
N Plath, M Toussaint, S Nakajima
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
Probabilistic inference for solving (PO) MDPs
M Toussaint, S Harmeling, A Storkey
University of Edinburgh, School of Informatics Research Report EDI-INF-RR-0934, 2006
Planning with noisy probabilistic relational rules
T Lang, M Toussaint
Journal of Artificial Intelligence Research 39, 1-49, 2010
Exploration in model-based reinforcement learning by empirically estimating learning progress
M Lopes, T Lang, M Toussaint, PY Oudeyer
Advances in neural information processing systems, 206-214, 2012
Hierarchical POMDP Controller Optimization by Likelihood Maximization.
M Toussaint, L Charlin, P Poupart
UAI 24, 562-570, 2008
Learning model-free robot control by a Monte Carlo EM algorithm
N Vlassis, M Toussaint, G Kontes, S Piperidis
Autonomous Robots 27 (2), 123-130, 2009
A sensorimotor map: Modulating lateral interactions for anticipation and planning
M Toussaint
Neural computation 18 (5), 1132-1155, 2006
Logic-geometric programming: An optimization-based approach to combined task and motion planning
M Toussaint
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
Scalable multiagent planning using probabilistic inference
A Kumar, S Zilberstein, M Toussaint
Twenty-Second International Joint Conference on Artificial Intelligence, 2011
Active learning for teaching a robot grounded relational symbols
J Kulick, M Toussaint, T Lang, M Lopes
Twenty-Third International Joint Conference on Artificial Intelligence, 2013
Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference
M Toussaint, N Plath, T Lang, N Jetchev
2010 IEEE International Conference on Robotics and Automation, 385-391, 2010
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