Department of Engineering

Prof. Carl Rasmussen - Publications

Number of items: 88.

Article

Bauer, M and Van Der Wilk, M and Rasmussen, CE (2016) Understanding probabilistic sparse Gaussian Process approximations. Advances in Neural Information Processing Systems. pp. 1533-1541. ISSN 1049-5258

Deisenroth, MP and Fox, D and Rasmussen, CE (2015) Gaussian processes for data-efficient learning in robotics and control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37. pp. 408-423. ISSN 0162-8828

Frigola, R and Lindsten, F and Schön, TB and Rasmussen, CE (2013) Bayesian inference and learning in Gaussian process state-space models with Particle MCMC. Advances in Neural Information Processing Systems. ISSN 1049-5258

Frigola, R and Rasmussen, CE (2013) Integrated pre-processing for bayesian nonlinear system identification with gaussian processes. Proceedings of the IEEE Conference on Decision and Control. pp. 5371-5376. ISSN 0191-2216

Cunningham, J and Ghahramani, Z and Rasmussen, CE (2012) Gaussian Processes for time-marked time-series data. Journal of Machine Learning Research, 22. pp. 255-263.

Deisenroth, MP and Turner, RD and Huber, MF and Hanebeck, UD and Rasmussen, CE (2012) Robust filtering and smoothing with gaussian processes. IEEE Transactions on Automatic Control, 57. pp. 1865-1871. ISSN 0018-9286

Turner, R and Rasmussen, CE (2012) Model based learning of sigma points in unscented Kalman filtering. Neurocomputing, 80. pp. 47-53. ISSN 0925-2312

Hall, J and Rasmussen, C and MacIejowski, J (2012) Modelling and control of nonlinear systems using Gaussian processes with partial model information. Proceedings of the IEEE Conference on Decision and Control. pp. 5266-5271. ISSN 0191-2216

Osborne, MA and Duvenaud, D and Garnett, R and Rasmussen, CE and Roberts, SJ and Ghahramani, Z (2012) Active learning of model evidence using Bayesian quadrature. Advances in Neural Information Processing Systems, 1. pp. 46-54. ISSN 1049-5258

Duvenaud, D and Nickisch, H and Rasmussen, CE (2011) Additive Gaussian processes. Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011.

McHutchon, A and Rasmussen, CE (2011) Gaussian Process training with input noise. Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011.

Hall, J and Rasmussen, CE and MacIejowski, J (2011) Reinforcement learning with reference tracking control in continuous state spaces. Proceedings of the IEEE Conference on Decision and Control. pp. 6019-6024. ISSN 0191-2216

Turner, R and Rasmussen, CE (2011) Model based learning of sigma points in unscented Kalman filtering. Neurocomputing. ISSN 0925-2312

Saatçi, Y and Turner, R and Rasmussen, CE (2010) Gaussian process change point models. ICML 2010 - Proceedings, 27th International Conference on Machine Learning. pp. 927-934.

Dilan, G and Carl Edward, R (2010) Dirichlet process gaussian mixture models: choice of the base distribution. Journal of Computer Science and Technology, 25. pp. 615-626. ISSN 1000-9000

Carl Edward, R and Hannes, N (2010) Gaussian processes for machine learning (GPML) toolbox. Journal of Machine Learning Research, 11. pp. 3011-3015. ISSN 1533-7928

Lazaro-Gredilla, M and Quinonero-Candela, J and Rasmussen, CE and Figueiras-Vidal, AR (2010) Sparse Spectrum Gaussian Process Regression. J MACH LEARN RES, 11. pp. 1865-1881. ISSN 1532-4435

Turner, R and Deisenroth, MP and Rasmussen, CE (2010) State-space inference and learning with Gaussian processes. Journal of Machine Learning Research, 9. pp. 868-875. ISSN 1532-4435

Turner, R and Rasmussen, CE (2010) Model based learning of sigma points in unscented Kalman filtering. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. pp. 178-183.

Nickisch, H and Rasmussen, CE (2010) Gaussian mixture modeling with Gaussian process latent variable models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6376 L. pp. 272-282. ISSN 0302-9743

Deisenroth, MP and Rasmussen, CE and Peters, J (2009) Gaussian process dynamic programming. Neurocomputing, 72. pp. 1508-1524. ISSN 0925-2312

Görür, D and Rasmussen, CE (2009) Nonparametric mixtures of factor analyzers. 2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009. pp. 708-711.

Hannes, N and C E, R (2008) Approximations for Binary Gaussian Process Classification. Journal of Machine Learning Research, 9. pp. 2035-2078. ISSN 1532-4435

Rasmussen, CE and de la Cruz, BJ and Ghahramani, Z and Wild, DL (2008) Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6. pp. 615-628. ISSN 1545-5963

Sonnenburg, S and Braun, ML and Ong, CS and Bengio, S and Bottou, L and Holmes, G and LeCun, Y and Müller, KR and Pereira, F and Rasmussen, CE and Rätch, G and Schölkopf, B and Smola, A and Vincent, P and Weston, J and Williamson, RC (2007) The need for open source software in machine learning. Jounal of Machine Learning Research, 8. pp. 2443-2466. ISSN 1533-7928

Kuss, M and Rasmussen, CE (2006) Assessing approximate inference for binary Gaussian process classification. Journal of Machine Learning Research, 6. pp. 1679-1704. ISSN 1532-4435

Pfingsten, T and Herrmann, D and Rasmussen, CE (2006) Model-based design analysis and yield optimization. IEEE Transactions on Semiconductor Manufacturing, 19. pp. 475-486. ISSN 0894-6507

Quiñonero Candela, J and Rasmussen, CE (2006) A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6. pp. 1939-1960. ISSN 1532-4435

Anderson, IK and Szymkowiak, A and Rasmussen, CE and Hanson, LG and Marstrand, JR and Larsson, HBW and Hansen, LK (2002) Perfusion quantification using Gaussian process deconvolution. Magnetic Resonance in Medicine, 48. pp. 351-361. ISSN 0740-3194

Hansen, LK and Rasmussen, CE (1994) Pruning from adaptive regularization. Neural Computation, 6. pp. 1223-1231. ISSN 0899-7667

Rasmussen, CE and Willshaw, DJ (1993) Presynaptic and postsynaptic competition in models for the development of neuromuscular connections. Biological Cybernetics, 68. pp. 409-419. ISSN 0340-1200

Garriga-Alonso, A and Aitchison, L and Rasmussen, CE Deep Convolutional Networks as shallow Gaussian Processes. arXiv. (Unpublished)

Rasmussen, CE and Nickisch, H Gaussian Processes for Machine Learning. (Unpublished)

Book

Rasmussen, CE and Williams, CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge, MA, USA, -.

Book Section

Rasmussen, CE and Deisenroth, MP (2008) Probabilistic inference for fast learning in control. In: Recent Advances in Reinforcement Learning. Lecture Notes in Computer Science, subseries: Lecture Notes in Artificial Intelligence . Springer, pp. 229-242.

Quiñonero Candela, J and Rasmussen, CE and Williams, CKI (2007) Approximation methods for Gaussian process regression. In: Large-Scale Kernel Machines. Neural Information Processing series . MIT Press, Cambridge, Massachusetts, USA, pp. 203-224.

Quiñonero Candela, J and Rasmussen, CE and Sinz, F and Bousquet, O and Scholkopf, B (2006) Evaluating predictive uncertainty challenge. In: Machine Learning Challenges. Lecture Notes in Computer Science, 3944 . Springer, Germany, pp. 1-27.

Quiñonero Candela, J and Rasmussen, CE (2005) Analysis of some methods for reduced rank Gaussian process regression. In: Switching and Learning in Feedback Systems. Lecture Notes in Computer Science: Theoretical Computer Science and General Issues, 3355 . Springer, Germany, pp. 98-127.

Rasmussen, CE (2004) Gaussian processes in machine learning. In: Advanced Lectures on Machine Learning. Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence, 3176 . Springer, Germany, pp. 63-71.

Conference or Workshop Item

Calliess, JM and Roberts, S and Rasmussen, CE and Maciejowski, J (2018) Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control. In: European Control Conference, 2018-6-12 to 2018-6-15, Limassol pp. 3167-3172..

Parmas, P and Rasmussen, CE and Peters, J and Doya, K (2018) PIPPS: Flexible model-based policy search robust to the curse of chaos. In: International Conference on Machine Learning, 2018-7-10 to 2018-7-15 pp. 6463-6472..

Van Der Wilk, M and Rasmussen, CE and Hensman, J (2017) Convolutional Gaussian processes. In: Neural Information Processing Systems 2017, 2017-12- to -- pp. 2850-2859..

McAllister, RT and Rasmussen, CE (2017) Data-efficient reinforcement learning in continuous state-action Gaussian-POMDPs. In: Neural Information Processing Systems, 2017-12- to -- pp. 2041-2050..

Calandra, R and Peters, J and Rasmussen, CE and Deisenroth, MP (2016) Manifold Gaussian Processes for regression. In: International Joint Conference on Neural Networks, -- to -- pp. 3338-3345..

Gal, Y and Van Der Wilk, M and Rasmussen, CE (2014) Distributed variational inference in sparse Gaussian process regression and latent variable models. In: UNSPECIFIED pp. 3257-3265..

Frigola, R and Lindsten, F and Schön, TB and Rasmussen, CE (2014) Identification of Gaussian process state-space models with particle stochastic approximation EM. In: UNSPECIFIED pp. 4097-4102..

Bischoff, B and Nguyen-Tuong, D and Van Hoof, H and McHutchon, A and Rasmussen, CE and Knoll, A and Peters, J and Deisenroth, MP (2014) Policy search for learning robot control using sparse data. In: UNSPECIFIED pp. 3882-3887..

Frigola, R and Chen, Y and Rasmussen, CE (2014) Variational Gaussian process state-space models. In: UNSPECIFIED pp. 3680-3688..

Deisenroth, MP and Rasmussen, CE and Fox, D (2012) Learning to control a low-cost manipulator using data-efficient reinforcement learning. In: UNSPECIFIED pp. 57-64..

Deisenroth, MP and Rasmussen, CE (2011) PILCO: A model-based and data-efficient approach to policy search. In: 28th International Conference on Machine Learning, ICML 2011, 2011-6-28 to 2011-7-2, Bellevue, Washington, USA pp. 465-473..

Deisenroth, MP and Peters, J and Rasmussen, CE (2008) Approximate dynamic programming with gaussian processes. In: American Control Conference 2008, ACC'08, 2008-6-11 to 2008-6-13, Seattle, Washington, USA.

Deisenroth, MP and Rasmussen, CE and Peters, J (2008) Model-based reinforcement learning with continuous states and actions. In: European Symposium on Artificial Neural Networks, Advances in Computational Intelligence and Learning (ESANN) 2008, 2008-4-23 to 2008-4-25, Bruges, Belgium.

Deisenroth, MP and Peters, J and Rasmussen, CE (2008) Approximate dynamic programming with Gaussian processes. In: UNSPECIFIED pp. 4480-4485..

Kuss, M and Rasmussen, CE (2006) Assessing approximations for Gaussian process classification. In: 19th Annual Conference on Neural Information Processing Systems (NIPS Workshop), 2005-12-9 to --, Whistler, Canada pp. 699-706..

Gorur, D and Jakel, F and Rasmussen, CE (2006) A choice model with infinitely many latent features. In: The 23rd International Conference on Machine Learning; ICML 2006, 2006-8- to --, Pittsburgh, PA, USA pp. 361-368..

Tanner, TG and Hill, NJ and Rasmussen, CE and Wichmann, FA (2005) Efficient adaptive sampling of the psychometric function by maximising information gain. In: 8th Conference onTubingen Perception, TWK' 05, 2005-- to --, Tubingen, Germany p.109-..

Rasmussen, CE and Quinonero Candela, J (2005) Healing the relevance vector machine through augmentation. In: The 22nd International Conference on Machine Learning: ICML 2005, 2005-8- to -- pp. 689-696..

Kocijan, J and Murray Smith, R and Rasmussen, CE and Girard, A (2004) Gaussian process model based predictive control. In: The American Control Conference v.3, 2004-6- to -- pp. 2214-2219..

Rasmussen, CE and Kuss, M (2004) Gaussian processes in reinforcement learning. In: 17th Annual Conference on Neural Information Processing Systems, NIPS'03, 2003-12- to --, British Columbia, Canada pp. 751-759..

Sinz, F and Quinonero Candela, J and Bakir, GH and Rasmussen, CE and Franz, MO (2004) Learning depth from stereo. In: The 26th DAGM Symposium on Pattern Recognition, 2004-8- to -- pp. 245-252..

Gorur, D and Rasmussen, CE and Tolias, AS and Sinz, F and Logothetis, NK (2004) Modelling spikes with mixtures of factor analysers. In: The 26th DAGM Symposium on Pattern Recognition, 2004-8- to -- pp. 391-398..

Eichhorn, J and Tolias, AS and Zien, A and Kuss, M and Rasmussen, CE and Weston, J and Logothetis, NK and Scholkopf, B (2004) Prediction on spike data using kernel algorithms. In: 17th Annual Conference on Neural Information Processing Systems, NIPS'03, 2003-12- to --, British Columbia, Canada pp. 1367-1374..

Franz, MO and Kwon, Y and Rasmussen, CE and Scholkopf, B (2004) Semi-supervised kernel regression using whitened function classes. In: The 26th DAGM Symposium on Pattern Recognition, 2004-8- to -- pp. 18-26..

Snelson, E and Rasmussen, CE and Ghahramani, Z (2004) Warped Gaussian processes. In: Neural Information Processing Systems, NIPS, 17th Annual Conference, 2003-12- to --, British Columbia, Canada pp. 337-344..

Dubey, A and Hwang, S and Rangel, C and Rasmussen, CE and Ghahramani, Z and Wild, DL (2004) Clustering protein sequence and structure space with infinite Gaussian mixture models. In: The Pacific Symposium on Biocomputing 2004, 2004-1-6 to 2004-1-10, Hawaii, HI, US pp. 399-410..

Murray Smith, RD and Sbarbaro, CE and Rasmussen, CE and Girard, A (2003) Adaptive, cautious, predictive control with Gaussian process priors. In: The 13th IFAC Symposium on System Identification; SYSID '03 v.3, 2003-8- to -- pp. 1155-1160..

Rasmussen, CE and Ghahramani, Z (2003) Bayesian Monte Carlo. In: 16th Annual Conference on Neural Information Processing Systems, NIPS'02, 2002-12- to --, British Columbia, Canada pp. 505-512..

Solak, E and Murray Smith, R and Leithead, WE and Leith, D and Rasmussen, CE (2003) Derivative observations in Gaussian process models of dynamic systems. In: 16th Annual Conference on Neural Information Processing Systems, NIPS'02, 2002-12- to --, British Columbia, Canada pp. 1057-1064..

Girard, A and Rasmussen, CE and Quiñonero Candela, J and Murray Smith, R (2003) Gaussian process priors with uncertain inputs - application to multiple-step ahead time series forecasting. In: 16th Annual Conference on Neural Information Processing Systems, NIPS'02, 2002-12- to --, British Columbia, Canada pp. 545-552..

Rasmussen, CE (2003) Gaussian processes to speed up hybrid Monte Carlo for expensive Bayesian integrals. In: Bayesian Statistics 7: the 7th Valencia International Meeting, -- to -- pp. 651-659..

Kocijan, J and Murray Smith, R and Rasmussen, CE and Likar, B (2003) Predictive control with Gaussian process models. In: The IEEE Region 8 Conference Eurocon 2003: The Computer as a Tool v.1, 2003-- to -- pp. 352-356..

Quiñonero Candela, J and Girard, A and Larsen, J and Rasmussen, CE (2003) Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting. In: 2003 IEEE International Workshop on Neural Networks for Signal Processing, 2003-- to --.

Kocijan, JB and Banko, B and Likar, A and Girard, A and Murray Smith, R and Rasmussen, CE (2003) A case based comparison of identification with neural network and Gaussian process models. In: The IFAC International Conference on Intelligent Control Systems and Signal Processing; (ICONS 2003), 2003-4- to --, Faro, Portugal pp. 129-134..

Quiñonero Candela, J and Girard, A and Larsen, J and Rasmussen, CE (2003) Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting. In: 28th IEEE International Conference on Acoustics Speech and Signal Processing, ICASSP 2003, 2003-4-6 to 2003-4-10, Hong Kong, China pp. 701-704..

Wild, DL and Rasmussen, CE and Ghahramani, Z and Cregg, J and de la Cruz, BJ and Kan, CC and Scanlon, K (2002) A Bayesian approach to modelling uncertainty in gene expression clusters. In: 3rd International Conference on Systems Biology, 2002-- to --, Stockholm, Sweden.

Rasmussen, CE and Ghahramani, Z (2002) Infinite mixtures of Gaussian process experts. In: Advances in Neural Information Processing Systems 14: the 2001 Neural Information Processing Systems (NIPS) Conference, 2001-- to -- pp. 881-888..

Beal, MJ and Ghahramani, Z and Rasmussen, CE (2002) The infinite hidden Markov model. In: Advances in Neural Information Processing Systems 14: the 2001 Neural Information Processing Systems (NIPS) Conference, 2001-- to --, British Columbia, Canada pp. 577-585..

Rasmussen, CE and Ghahramani, Z (2001) Occam's razor. In: 14th Annual Conference on Advances on Neural Information Processing Systems, NIPS 2000, 2000-11- to --, Denver, CO, US pp. 294-300..

Højen Sørensen, PA and Rasmussen, CE and Hansen, LK (2000) Bayesian modelling of fMRI time series. In: 13th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 99, 1999-12- to -- pp. 754-760..

Rasmussen, CE (2000) The infinite Gaussian mixture model. In: 13th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 99, 1999-12- to -- pp. 554-560..

Williams, CKI and Rasmussen, CE (1996) Gaussian processes for regression. In: 9th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 95, 1995-11- to --, Denver, Colorado, USA.

Rasmussen, CE (1996) A practical Monte Carlo implementation of Bayesian learning. In: 9th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 95, 1995-11- to --, Denver, Colorado, USA pp. 598-604..

Trapp, M and Peharz, R and Rasmussen, CE and Pernkopf, F Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks. In: 35th International Conference on Machine Learning, -- to --, Stockholm, Sweden. (Unpublished)

Monograph

Kuss, M and Pfingsten, T and Csato, L and Rasmussen, CE (2005) Approximate inference for robust Gaussian process regression. Technical Report. Max Planck Institute: Biological Cybernetics, Tübingen, Germany.

Quiñonero Candela, J and Girard, A and Rasmussen, CE (2003) Prediction at an uncertain input for Gaussian processes and relevance vector machines - application to multiple-step ahead time-series forecasting. Technical Report. Technical University of Denmark, Denmark.

Williams, CKI and Rasmussen, CE and Scwaighofer, A and Tresp, V (2002) Observations on the Nystrom method for Gaussian process prediction. Technical Report. University of Edinburgh and University College London, London, UK.

Rasmussen, CE and Neal, RM and Hinton, GE and Van Camp, D and Revow, M and Ghahramani, Z and Kustra, R and Tibshirani, R (1996) The delve manual. Technical Report. University of Toronto: Department of Computer Science, Toronto, Canada.

Thesis

Rasmussen, CE (1996) Evaluation of Gaussian processes and other methods for non-linear regression. PhD thesis, UNSPECIFIED.

This list was generated on Thu Oct 17 01:33:09 2019 BST.