Carl Edward Rasmussen
Carl Edward Rasmussen
Professor of Machine Learning, University of Cambridge
Verified email at cam.ac.uk - Homepage
TitleCited byYear
PIPPS: Flexible model-based policy search robust to the curse of chaos
P Parmas, CE Rasmussen, J Peters, K Doya
arXiv preprint arXiv:1902.01240, 2019
122019
Non-Factorised Variational Inference in Dynamical Systems
AD Ialongo, M van der Wilk, J Hensman, CE Rasmussen
arXiv preprint arXiv:1812.06067, 2018
32018
Closed-form inference and prediction in Gaussian process state-space models
AD Ialongo, M van der Wilk, CE Rasmussen
arXiv preprint arXiv:1812.03580, 2018
42018
Deep convolutional networks as shallow Gaussian processes
A Garriga-Alonso, L Aitchison, CE Rasmussen
arXiv preprint arXiv:1808.05587, 2018
352018
Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control
JP Calliess, S Roberts, C Rasmussen, J Maciejowski
2018 European Control Conference (ECC), 1-6, 2018
22018
Convolutional Gaussian Processes
M van der Wilk, CE Rasmussen, J Hensman
Advances in Neural Information Processing Systems, 2845-2854, 2017
342017
Understanding Probabilistic Sparse Gaussian Process Approximations
MS Bauer, M van der Wilk, CE Rasmussen
arXiv preprint arXiv:1606.04820, 2016
812016
Data-efficient reinforcement learning in continuous-state POMDPs
R McAllister, CE Rasmussen
arXiv preprint arXiv:1602.02523, 2016
52016
Gaussian Processes for Data-Efficient Learning in Robotics and Control
M Deisenroth, D Fox, C Rasmussen
IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 408-423, 2015
3382015
Policy search for learning robot control using sparse data
B Bischoff, D Nguyen-Tuong, H van Hoof, A McHutchon, CE Rasmussen, ...
Robotics and Automation (ICRA), 2014 IEEE International Conference on, 3882-3887, 2014
182014
Manifold Gaussian Processes for Regression
R Calandra, J Peters, CE Rasmussen, MP Deisenroth
arXiv preprint arXiv:1402.5876, 2014
852014
Variational Gaussian process state-space models
R Frigola, Y Chen, C Rasmussen
Advances in Neural Information Processing Systems, 3680-3688, 2014
872014
Distributed variational inference in sparse Gaussian process regression and latent variable models
Y Gal, M van der Wilk, C Rasmussen
Advances in Neural Information Processing Systems, 3257-3265, 2014
1272014
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
R Frigola, F Lindsten, TB Schön, CE Rasmussen
arXiv preprint arXiv:1312.4852, 2013
202013
PILCO Code Documentation v0. 9
MP Deisenroth, A McHutchon, J Hall, CE Rasmussen
2013
Automated Bayesian System Identification with NARX Models
R Frigola, CE Rasmussen
arXiv preprint arXiv:1303.2912, 2013
22013
Bayesian inference and learning in Gaussian process state-space models with particle MCMC
R Frigola, F Lindsten, TB Schön, C Rasmussen
Advances in Neural Information Processing Systems, 3156-3164, 2013
782013
Active Learning of Model Evidence Using Bayesian Quadrature
MA Osborne, D Duvenaud, R Garnett, CE Rasmussen, SJ Roberts, ...
Advances in Neural Processing Systems 25, 46-54, 2013
712013
Modelling and control of nonlinear systems using Gaussian processes with partial model information
J Hall, C Rasmussen, J Maciejowski
Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, 5266-5271, 2012
192012
Machine Learning Challenges: evaluating predictive uncertainty, visual object classification and recognising textual entailment}
J Quinonero Candela, I Dagan, B Magnini, F Lauria, ...
Proceedings of the First Pascal Machine Learning Challenges Workshop on …, 2012
16*2012
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Articles 1–20