Marc Toussaint

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Danny DriessTU BerlinVerified email at ipvs.uni-stuttgart.de
Sethu VijayakumarProfessor of Robotics, University of Edinburgh; Programme Director at The Alan Turing Institute, UKVerified email at ed.ac.uk
Christian IgelDepartment of Computer Science, University of CopenhagenVerified email at diku.dk
Manuel LopesInstituto Superior Tecnico, INESC-IDVerified email at tecnico.ulisboa.pt
Ozgur S. OguzBilkent UniversityVerified email at cs.bilkent.edu.tr
Andreas OrtheyRealtime RoboticsVerified email at rtr.ai
Nikolay JetchevSenior Research Scientist at ZalandoVerified email at zalando.de
Jung-Su HaLearning & Intelligent Systems, TU Berlin, Max Planck Institute for Intelligent SystemsVerified email at tu-berlin.de
Jim MainpriceMercedes-Benz AG R&D - Automated DrivingVerified email at mercedes-benz.com
Peter EnglertRealtime RoboticsVerified email at rtr.ai
Michael GiengerHonda Research Institute EuropeVerified email at honda-ri.de
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
Philipp KratzerUniversity of StuttgartVerified email at ipvs.uni-stuttgart.de
Duy Nguyen-TuongBosch Center for AI, GermanyVerified email at robot-learning.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
Valentin N. HartmannUniversity of StuttgartVerified email at ipvs.uni-stuttgart.de
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu

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	501	2006
Robot trajectory optimization using approximate inference M Toussaint Proceedings of the 26th annual international conference on machine learning …, 2009	376	2009
Planning as inference M Botvinick, M Toussaint Trends in cognitive sciences 16 (10), 485-488, 2012	312	2012
On stochastic optimal control and reinforcement learning by approximate inference K Rawlik, M Toussaint, S Vijayakumar Proceedings of Robotics: Science and Systems VIII, 2012	301	2012
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	261	2018
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	257	2006
Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning. M Toussaint IJCAI, 1930-1936, 2015	251	2015
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 25, 2012	211	2012
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	177	2011
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	174	2009
A no-free-lunch theorem for non-uniform distributions of target functions C Igel, M Toussaint Journal of Mathematical Modelling and Algorithms 3, 313-322, 2005	168	2005
Probabilistic inference as a model of planned behavior. M Toussaint Künstliche Intell. 23 (3), 23-29, 2009	143	2009
On classes of functions for which no free lunch results hold C Igel, M Toussaint arXiv preprint cs/0108011, 2001	129	2001
Probabilistic recurrent state-space models A Doerr, C Daniel, M Schiegg, NT Duy, S Schaal, M Toussaint, ... International conference on machine learning, 1280-1289, 2018	109	2018
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	109	2006
Hierarchical POMDP Controller Optimization by Likelihood Maximization. M Toussaint, L Charlin, P Poupart UAI 24, 562-570, 2008	108	2008
Inverse KKT: Learning cost functions of manipulation tasks from demonstrations P Englert, NA Vien, M Toussaint The International Journal of Robotics Research 36 (13-14), 1474-1488, 2017	100	2017
Safe exploration for active learning with Gaussian processes J Schreiter, D Nguyen-Tuong, M Eberts, B Bischoff, H Markert, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015	96	2015
Active Learning for Teaching a Robot Grounded Relational Symbols. J Kulick, M Toussaint, T Lang, M Lopes IJCAI, 1451-1457, 2013	96	2013
Planning with noisy probabilistic relational rules T Lang, M Toussaint Journal of Artificial Intelligence Research 39, 1-49, 2010	92	2010

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