Deep reinforcement learning: A brief survey K Arulkumaran, MP Deisenroth, M Brundage, AA Bharath IEEE Signal Processing Magazine 34 (6), 26-38, 2017 | 4228* | 2017 |
PILCO: A model-based and data-efficient approach to policy search MP Deisenroth, CE Rasmussen International Conference on Machine Learning (ICML), 465-472, 2011 | 1966 | 2011 |
A Survey on Policy Search for Robotics MP Deisenroth, G Neumann, J Peters Foundations and Trends in Robotics 2 (1-2), 1-142, 2013 | 1239 | 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 | 836 | 2015 |
Mathematics for Machine Learning MP Deisenroth, AA Faisal, CS Ong Cambridge University Press, 2020 | 759 | 2020 |
Doubly Stochastic Variational Inference for Deep Gaussian Processes H Salimbeni, M Deisenroth Advances in Neural Information Processing Systems (NeurIPS), 2017 | 472 | 2017 |
Distributed Gaussian Processes MP Deisenroth, JW Ng International Conference on Machine Learning (ICML), 2015 | 419 | 2015 |
Bayesian optimization for learning gaits under uncertainty: An experimental comparison on a dynamic bipedal walker R Calandra, A Seyfarth, J Peters, MP Deisenroth Annals of Mathematics and Artificial Intelligence 76, 5-23, 2016 | 366 | 2016 |
Manifold Gaussian processes for regression R Calandra, J Peters, CE Rasmussen, MP Deisenroth 2016 International joint conference on neural networks (IJCNN), 3338-3345, 2016 | 341 | 2016 |
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning MP Deisenroth, CE Rasmussen, D Fox Robotics: Science & Systems (RSS), 2011 | 310 | 2011 |
Gaussian process dynamic programming MP Deisenroth, CE Rasmussen, J Peters Neurocomputing 72 (7-9), 1508-1524, 2009 | 281 | 2009 |
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control S Kamthe, MP Deisenroth International Conference on Artificial Intelligence and Statistics (AISTATS), 2018 | 278 | 2018 |
Maximizing acquisition functions for Bayesian optimization JT Wilson, F Hutter, MP Deisenroth Neural Information Processing Systems (NeurIPS), 2018 | 274 | 2018 |
Efficient reinforcement learning using Gaussian processes MP Deisenroth KIT Scientific Publishing, 2010 | 272 | 2010 |
From pixels to torques: Policy learning with deep dynamical models N Wahlström, TB Schön, MP Deisenroth arXiv preprint arXiv:1502.02251, 2015 | 220 | 2015 |
Neural embeddings of graphs in hyperbolic space BP Chamberlain, J Clough, MP Deisenroth arXiv preprint arXiv:1705.10359, 2017 | 196 | 2017 |
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 | 193 | 2013 |
Analytic moment-based Gaussian process filtering MP Deisenroth, MF Huber, UD Hanebeck International Conference on Machine Learning (ICML), 225-232, 2009 | 179 | 2009 |
Meta Reinforcement Learning with Latent Variable Gaussian Processes S Sæmundsson, K Hofmann, MP Deisenroth Uncertainty in Artificial Intelligence (UAI), 2018 | 174 | 2018 |
Multi-Task Policy Search for Robotics MP Deisenroth, P Englert, J Peters, D Fox IEEE International Conference on Robotics and Automation (ICRA), 2014 | 155 | 2014 |