Marc Deisenroth

Cited by

	All	Since 2016
Citations	7218	5974
h-index	37	32
i10-index	62	58

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Co-authors

Jan PetersProfessor at Technische Universität Darmstadt and Researcher at MPI for Intelligent SystemsVerified email at ias.tu-darmstadt.de
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Roberto CalandraFacebook AI ResearchVerified email at fb.com
Dieter FoxProfessor of Computer Science and Engineering, University of Washington, and Nvidia ResearchVerified email at cs.washington.edu
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Verified email at robot-learning.de
Hugh SalimbeniImperial College LondonVerified email at ic.ac.uk
Uwe D. HanebeckKarlsruhe Institute of Technology (KIT)Verified email at ieee.org
Benjamin Paul ChamberlainTwitterVerified email at twitter.com
Andre SeyfarthProfessor of Sports Biomechanics, TU DarmstadtVerified email at sport.tu-darmstadt.de
Anil Anthony BharathDept of Bioengineering, Imperial CollegeVerified email at imperial.ac.uk
Kai ArulkumaranArayaVerified email at araya.org
Miles BrundageResearch Scientist (Policy), OpenAIVerified email at asu.edu
Marco HuberUniversity of StuttgartVerified email at ieee.org
Peter EnglertUniversity of Southern CaliforniaVerified email at usc.edu
Andras KupcsikBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Niklas WahlströmAssistant Professor, Uppsala University, SwedenVerified email at it.uu.se
Sanket KamtheImperial College LondonVerified email at imperial.ac.uk
Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
Ruth MisenerImperial College LondonVerified email at imperial.ac.uk
Stefanos EleftheriadisSenior machine learning researcher at SecondmindVerified email at secondmind.ai

Marc Deisenroth

University College London

Verified email at ucl.ac.uk - Homepage

machine learning Gaussian processes robotics reinforcement learning Bayesian optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
PILCO: A model-based and data-efficient approach to policy search MP Deisenroth, CE Rasmussen International Conference on Machine Learning (ICML), 465-472, 2011	1034	2011
Deep reinforcement learning: A brief survey K Arulkumaran, MP Deisenroth, M Brundage, AA Bharath IEEE Signal Processing Magazine 34 (6), 26-38, 2017	1001*	2017
A Survey on Policy Search for Robotics MP Deisenroth, G Neumann, J Peters Foundations and Trends in Robotics 2 (1-2), 1-142, 2013	800	2013
Gaussian Processes for Data-Efficient Learning in Robotics and Control MP Deisenroth, D Fox, CE Rasmussen Transactions on Pattern Analysis and Machine Intelligence 37 (2), 408-423, 2015	460	2015
Gaussian process dynamic programming MP Deisenroth, CE Rasmussen, J Peters Neurocomputing 72 (7-9), 1508-1524, 2009	240	2009
Distributed Gaussian Processes MP Deisenroth, JW Ng International Conference on Machine Learning (ICML), 2015	237	2015
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning MP Deisenroth, CE Rasmussen, D Fox Robotics: Science & Systems (RSS), 2011	203	2011
Doubly Stochastic Variational Inference for Deep Gaussian Processes H Salimbeni, M Deisenroth Advances in Neural Information Processing Systems (NIPS), 2017	194	2017
Bayesian optimization for learning gaits under uncertainty R Calandra, A Seyfarth, J Peters, MP Deisenroth Annals of Mathematics and Artificial Intelligence 76 (1), 5-23, 2016	175	2016
Efficient reinforcement learning using Gaussian processes MP Deisenroth KIT Scientific Publishing, 2010	174	2010
Analytic moment-based Gaussian process filtering MP Deisenroth, MF Huber, UD Hanebeck International Conference on Machine Learning (ICML), 225-232, 2009	153	2009
Manifold Gaussian processes for regression R Calandra, J Peters, CE Rasmussen, MP Deisenroth 2016 International Joint Conference on Neural Networks (IJCNN), 3338-3345, 2016	145	2016
Probabilistic Movement Modeling for Intention Inference in Human-Robot Interaction Z Wang, K Mülling, MP Deisenroth, HB Amor, D Vogt, B Schölkopf, ... International Journal of Robotics Research, 2013	136	2013
From pixels to torques: Policy learning with deep dynamical models N Wahlström, TB Schön, MP Deisenroth arXiv preprint arXiv:1502.02251, 2015	128	2015
Robust filtering and smoothing with Gaussian processes MP Deisenroth, RD Turner, MF Huber, UD Hanebeck, CE Rasmussen IEEE Transactions on Automatic Control 57 (7), 1865-1871, 2011	117	2011
Data-efficient generalization of robot skills with contextual policy search A Kupcsik, M Deisenroth, J Peters, G Neumann Proceedings of the AAAI conference on artificial intelligence 27 (1), 2013	109	2013
Multi-Task Policy Search for Robotics MP Deisenroth, P Englert, J Peters, D Fox IEEE International Conference on Robotics and Automation (ICRA), 2014	97	2014
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control S Kamthe, MP Deisenroth International Conference on Artificial Intelligence and Statistics (AISTATS), 2018	96	2018
Neural embeddings of graphs in hyperbolic space BP Chamberlain, J Clough, MP Deisenroth arXiv preprint arXiv:1705.10359, 2017	96	2017
State-Space Inference and Learning with Gaussian Processes RD Turner, MP Deisenroth, CE Rasmussen International Conference on Artificial Intelligence and Statistics, 868-875, 2010	91	2010

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