Marc Toussaint

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Christian IgelDepartment of Computer Science, University of CopenhagenVerified email at diku.dk
Sethu VijayakumarProfessor of Robotics, University of Edinburgh; Programme Director at The Alan Turing Institute, UKVerified email at ed.ac.uk
Nikolay JetchevSenior Research Scientist at ZalandoVerified email at zalando.de
Manuel LopesInstituto Superior Tecnico, INESC-IDVerified email at tecnico.ulisboa.pt
Danny DriessUniversity of StuttgartVerified email at ipvs.uni-stuttgart.de
Michael GiengerHonda Research Institute EuropeVerified email at honda-ri.de
Peter EnglertUniversity of Southern CaliforniaVerified email at usc.edu
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceVerified email at google.com
Ngo Anh VienBosch Center for Artificial Intelligence (BCAI)Verified email at bosch.com
Duy Nguyen-TuongBosch Center for AI, GermanyVerified email at robot-learning.de
Jim MainpriceInterim Professor of Computer Science, Stuttgart UniversityVerified email at ipvs.uni-stuttgart.de
Johannes KulickApplied Scientist at Amazon BerlinVerified email at amazon.com
Konrad RawlikPostdoctoral Fellow, Roslin Institute, University of EdinburghVerified email at roslin.ed.ac.uk
Andreas OrtheyMax Planck Institute for Intelligent SystemsVerified email at is.mpg.de
Ozgur S. OguzUni Stuttgart & MPI ISVerified email at ipvs.uni-stuttgart.de
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Nathan RatliffNVIDIAVerified email at nvidia.com
Akshat KumarAssociate Professor, School of Information Systems, Singapore ManagementVerified email at smu.edu.sg
Robert LieckEPFLVerified email at epfl.ch

Marc Toussaint

Professor of Computer Science, TU Berlin, Germany

Verified email at tu-berlin.de - Homepage

Robotics Robot Learning Artificial Intelligence


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Using machine learning to focus iterative optimization F Agakov, E Bonilla, J Cavazos, B Franke, G Fursin, MFP O'Boyle, ... International Symposium on Code Generation and Optimization (CGO'06), 11 pp.-305, 2006	477	2006
Robot trajectory optimization using approximate inference M Toussaint Proceedings of the 26th annual international conference on machine learning …, 2009	292	2009
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	233	2013
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	223	2006
Planning as inference M Botvinick, M Toussaint Trends in cognitive sciences 16 (10), 485-488, 2012	202	2012
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	157	2005
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	152	2011
Exploration in model-based reinforcement learning by empirically estimating learning progress M Lopes, T Lang, M Toussaint, PY Oudeyer Neural Information Processing Systems (NIPS), 2012	151	2012
Logic-geometric programming: An optimization-based approach to combined task and motion planning M Toussaint Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015	136	2015
Differentiable physics and stable modes for tool-use and manipulation planning MA Toussaint, KR Allen, KA Smith, JB Tenenbaum Robotics: Science and Systems Foundation, 2018	135	2018
Probabilistic inference as a model of planned behavior. M Toussaint Künstliche Intell. 23 (3), 23-29, 2009	134	2009
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	126	2009
On classes of functions for which no free lunch results hold C Igel, M Toussaint arXiv preprint cs/0108011, 2001	125	2001
Hierarchical POMDP Controller Optimization by Likelihood Maximization. M Toussaint, L Charlin, P Poupart UAI 24, 562-570, 2008	109	2008
Probabilistic inference for solving (PO) MDPs M Toussaint, S Harmeling, A Storkey Technical Report EDI-INF-RR-0934, School of Informatics, University of Edinburgh, 2006	103	2006
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	89	2009
Planning with noisy probabilistic relational rules T Lang, M Toussaint Journal of Artificial Intelligence Research 39, 1-49, 2010	88	2010
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	81	2013
Fast motion planning from experience: trajectory prediction for speeding up movement generation N Jetchev, M Toussaint Autonomous Robots 34 (1), 111-127, 2013	73	2013
Scalable multiagent planning using probabilistic inference A Kumar, S Zilberstein, M Toussaint Twenty-Second International Joint Conference on Artificial Intelligence, 2011	72	2011

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