Publications by Dr C.E. Rasmussen

Article

Rasmussen, C.E. and de la Cruz, B.J. and Ghahramani, Z. and Wild, D.L. (2008) Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6 (4). pp. 615-628. ISSN 1545-5963

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

Sonnenburg, S. and Braun, M.L. and Ong, C.S. and Bengio, S. and Bottou, L. and Holmes, G. and LeCun, Y. and Müller, K-R. and Pereira, F. and Rasmussen, C.E. and Rätch, G. and Schölkopf, B. and Smola, A. and Vincent, P. and Weston, J. and Williamson, R.C. (2007) The need for open source software in machine learning Jounal of Machine Learning Research, 8 (10). pp. 2443-2466. ISSN 1533-7928

Pfingsten, T. and Herrmann, D. and Rasmussen, C.E. (2006) Model-based design analysis and yield optimization IEEE Transactions on Semiconductor Manufacturing, 19 (4). pp. 475-486. ISSN 0894-6507

Quiñonero-Candela, J. and Rasmussen, C.E. (2006) A unifying view of sparse approximate Gaussian process regression Journal of Machine Learning Research, 6 (2). pp. 1939-1960. ISSN 1532-4435

Kuss, M. and Rasmussen, C.E. (2006) Assessing approximate inference for binary Gaussian process classification Journal of Machine Learning Research, 6 (2). pp. 1679-1704. ISSN 1532-4435

Anderson, I.K. and Szymkowiak, A. and Rasmussen, C.E. and Hanson, L.G. and Marstrand, J.R. and Larsson, H.B.W. and Hansen, L.K. (2002) Perfusion quantification using Gaussian process deconvolution Magnetic Resonance in Medicine, 48 (2). pp. 351-361. ISSN 0740-3194

Hansen, L.K. and Rasmussen, C.E. (1994) Pruning from adaptive regularization Neural Computation, 6 (6). pp. 1223-1231. ISSN 0899-7667

Rasmussen, C.E. and Willshaw, D.J. (1993) Presynaptic and postsynaptic competition in models for the development of neuromuscular connections Biological Cybernetics, 68 (5). pp. 409-419. ISSN 0340-1200 (Print) 1432-0770 (Online)

Deisenroth, M.P. and Rasmussen, C.E. and Peters, J. Gaussian process dynamic programming Neurocomputing, 72 (7-9). pp. 1508-1524. ISSN 0925-2312 (In Press)

Book

Rasmussen, C.E. and Williams, C.K.I. (2006) Gaussian processes for machine learning MIT Press, Cambridge, MA, USA. ISBN 9780262182539, 026218253X

Rasmussen, C.E. and Bülthoff, H.H. and Giese, M. and Scholkopf, B., eds. (2004) Pattern Recognition: Proceedings of the 26th DAGM Symposium on Pattern Recognition held in Tubingen, Germany, August 2004 Lecture Notes in Computer Science 3175. Springer, Berlin, Germany. ISBN 9783540229452, 3540229450

Book Section

Rasmussen, C.E. and Deisenroth, M.P. (2008) Probabilistic inference for fast learning in control In: Girgin, S. and Loth, M. and Munos, R. and Preux, P. and Ryabko, D., (eds.) Recent Advances in Reinforcement Learning. Lecture Notes in Computer Science, subseries: Lecture Notes in Artificial Intelligence 5323. Springer, pp. 229-242. ISBN 9783540897217

Quiñonero-Candela, J. and Rasmussen, C.E. and Williams, C.K.I. (2007) Approximation methods for Gaussian process regression In: Bottou, L. and Chapelle, O. and DeCoste, D. and Weston, J., (eds.) Large-Scale Kernel Machines. Neural Information Processing series. MIT Press, Cambridge, Massachusetts, USA, pp. 203-224. ISBN 0262026252, 9780262026253

Quiñonero-Candela, J. and Rasmussen, C.E. and Sinz, F. and Bousquet, O. and Scholkopf, B. (2006) Evaluating predictive uncertainty challenge In: Quiñonero-Candela, J. and Dagan, I. and Magnini, B. and d'Alche-Buc, F., (eds.) Machine Learning Challenges. , (3944), Lecture Notes in Computer Science. Springer, Germany, pp. 1-27. ISBN 3540334270

Quiñonero-Candela, J. and Rasmussen, C.E. (2005) Analysis of some methods for reduced rank Gaussian process regression In: Murray-Smith, R. and Shorten, R., (eds.) Switching and Learning in Feedback Systems. , (3355), Lecture Notes in Computer Science: Theoretical Computer Science and General Issues. Springer, Germany, pp. 98-127. ISBN 9783540244578

Rasmussen, C.E. (2004) Gaussian processes in machine learning In: Bousquet, O. and Von Luxburg, U. and Ratsch, G., (eds.) Advanced Lectures on Machine Learning. , (3176), Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence. Springer, Germany, pp. 63-71. ISBN 9783540231226

Conference or Workshop Item

Gorur, D. and Jakel, F. and Rasmussen, C.E. (2006) A choice model with infinitely many latent features In: The 23rd International Conference on Machine Learning; ICML 2006, August 2006, Pittsburgh, PA, USA.

Kuss, M. and Rasmussen, C.E. (2006) Assessing approximations for Gaussian process classification In: 19th Annual Conference on Neural Information Processing Systems (NIPS Workshop), 9 Dec 2005, Whistler, Canada.

Tanner, T.G. and Hill, N.J. and Rasmussen, C.E. and Wichmann, F.A. (2005) Efficient adaptive sampling of the psychometric function by maximising information gain In: IEEE Workshop on Automatic Speech Recognition and Understanding 1999, 1999, Keystone, Colorado.

Rasmussen, C.E. and Quinonero Candela, J. (2005) Healing the relevance vector machine through augmentation In: The 22nd International Conference on Machine Learning: ICML 2005, August 2005.

Dubey, A. and Hwang, S. and Rangel, C. and Rasmussen, C.E. and Ghahramani, Z. and Wild, D.L. (2004) Clustering protein sequence and structure space with infinite Gaussian mixture models In: The Pacific Symposium on Biocomputing 2004, 6-10 January 2004, Hawaii, HI, US.

Gorur, D. and Rasmussen, C.E. and Tolias, A.S. and Sinz, F. and Logothetis, N.K. (2004) Modelling spikes with mixtures of factor analysers In: The 26th DAGM Symposium on Pattern Recognition, August 2004.

Sinz, F. and Quinonero Candela, J. and Bakir, G.H. and Rasmussen, C.E. and Franz, M.O. (2004) Learning depth from stereo In: The 26th DAGM Symposium on Pattern Recognition, August 2004.

Kocijan, J. and Murray-Smith, R. and Rasmussen, C.E. and Girard, A. (2004) Gaussian process model based predictive control In: The American Control Conference v.3, June 2004.

Franz, M.O. and Kwon, Y. and Rasmussen, C.E. and Scholkopf, B. (2004) Semi-supervised kernel regression using whitened function classes In: The 26th DAGM Symposium on Pattern Recognition, August 2004.

Eichhorn, J. and Tolias, A.S. and Zien, A. and Kuss, M. and Rasmussen, C.E. and Weston, J. and Logothetis, N.K. and Scholkopf, B. (2004) Prediction on spike data using kernel algorithms In: 17th Annual Conference on Neural Information Processing Systems, NIPS'03, December 2003, British Columbia, Canada.

Snelson, E. and Rasmussen, C.E. and Ghahramani, Z. (2004) Warped Gaussian processes In: Neural Information Processing Systems, NIPS, 17th Annual Conference, December 2003, British Columbia, Canada.

Rasmussen, C.E. and Kuss, M. (2004) Gaussian processes in reinforcement learning In: 17th Annual Conference on Neural Information Processing Systems, NIPS'03, December 2003, British Columbia, Canada.

Quiñonero-Candela, J. and Girard, A. and Larsen, J. and Rasmussen, C.E. (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, 6-10 April 2003, Hong Kong, China.

Murray-Smith, R.D. and Sbarbaro, C.E. and Rasmussen, C.E. and Girard, A. (2003) Adaptive, cautious, predictive control with Gaussian process priors In: The 13th IFAC Symposium on System Identification; SYSID '03 v.3, August 2003.

Rasmussen, C.E. (2003) Gaussian processes to speed up hybrid Monte Carlo for expensive Bayesian integrals In: Bayesian Statistics 7: the 7th Valencia International Meeting, .

Kocijan, J. and Murray-Smith, R. and Rasmussen, C.E. 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.

Quiñonero-Candela, J. and Girard, A. and Larsen, J. and Rasmussen, C.E. (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.

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

Kocijan, J.B. and Banko, B. and Likar, A. and Girard, A. and Murray-Smith, R. and Rasmussen, C.E. (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), April 2003, Faro, Portugal.

Rasmussen, C.E. and Ghahramani, Z. (2003) Bayesian Monte Carlo In: 16th Annual Conference on Neural Information Processing Systems, NIPS'02, December 2002, British Columbia, Canada.

Girard, A. and Rasmussen, C.E. 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, December 2002, British Columbia, Canada.

Rasmussen, C.E. 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.

Beal, M.J. and Ghahramani, Z. and Rasmussen, C.E. (2002) The infinite hidden Markov model In: Advances in Neural Information Processing Systems 14: the 2001 Neural Information Processing Systems (NIPS) Conference, 2001, British Columbia, Canada.

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

Rasmussen, C.E. and Ghahramani, Z. (2001) Occam's razor In: 14th Annual Conference on Advances on Neural Information Processing Systems, NIPS 2000, November 2000, Denver, CO, US.

Højen-Sørensen, P.A. and Rasmussen, C.E. and Hansen, L.K. (2000) Bayesian modelling of fMRI time series In: 13th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 99, December 1999.

Rasmussen, C.E. (2000) The infinite Gaussian mixture model In: 13th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 99, December 1999.

Williams, C.K.I. and Rasmussen, C.E. (1996) Gaussian processes for regression In: 9th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 95, November 1995, Denver, Colorado, USA.

Rasmussen, C.E. (1996) A practical Monte Carlo implementation of Bayesian learning In: 9th Annual Conference on Advances in Neural Information Processing Systems, NIPS' 95, November 1995, Denver, Colorado, USA.

Deisenroth, M.P. and Peters, J. and Rasmussen, C.E. Approximate dynamic programming with gaussian processes In: American Control Conference 2008, ACC'08, 11-13 June 2008, Seattle, Washington, USA. (In Press)

Deisenroth, M.P. and Rasmussen, C.E. and Peters, J. Model-based reinforcement learning with continuous states and actions In: European Symposium on Artificial Neural Networks, Advances in Computational Intelligence and Learning (ESANN) 2008, 23-25 April 2008, Bruges, Belgium. (In Press)

Monograph

Kuss, M. and Pfingsten, T. and Csato, L. and Rasmussen, C.E. (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, C.E. (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, C.K.I. and Rasmussen, C.E. 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, C.E. and Neal, R.M. and Hinton, G.E. 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, C.E. (1996) Evaluation of Gaussian processes and other methods for non-linear regression PhD thesis, University of Toronto.