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Nobody
Carl Edward Rasmussen
Dr.
Position: Research Scientist
Fax: +49-7071-601-552

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2014
Conference Papers
2011
Conference Papers
  • MP. Deisenroth, CE. Rasmussen (2011). PILCO: A Model-Based and Data-Efficient Approach to Policy Search In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, (Ed) L Getoor and T Scheffer, Omnipress, 465-472
  • D. Duvenaud, H. Nickisch, CA. Rasmussen (2011). Additive Gaussian Processes In: Advances in Neural Information Processing Systems 24, (Ed) J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger, 226-234, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
2010
Articles
  • M. Lázaro-Gredilla, J. Quiñonero-Candela, CE. Rasmussen, AR. Figueiras-Vidal (2010). Sparse Spectrum Gaussian Process Regression Journal of Machine Learning Research, 11, 1865-1881
Conference Papers
  • H. Nickisch, CE. Rasmussen (2010). Gaussian Mixture Modeling with Gaussian Process Latent Variable Models In: Pattern Recognition, (Ed) Goesele, M. , S. Roth, A. Kuijper, B. Schiele, K. Schindler, Pattern Recognition: 32nd DAGM Symposium, Springer, Deutsche Arbeitsgemeinschaft für Mustererkennung, Berlin, Germany, 271-282, ISBN: 978-3-642-15986-2, 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM 2010)
Technical Reports
2009
Articles
  • CE. Rasmussen, BJ. Cruz, Z. Ghahramani, DL. Wild (2009). Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6, (4), 615-628
2008
Articles
Conference Papers
  • MP. Deisenroth, J. Peters, CE. Rasmussen (2008). Approximate Dynamic Programming with Gaussian Processes In: ACC 2008, Proceedings of the 2008 American Control Conference (ACC 2008), IEEE Service Center, Piscataway, NJ, USA, 4480-4485, 2008 American Control Conference
  • CE. Rasmussen, MP. Deisenroth (2008). Probabilistic Inference for Fast Learning in Control In: EWRL 2008, (Ed) Girgin, S. , M. Loth, R. Munos, P. Preux, D. Ryabko, Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008), Springer, Berlin, Germany, 229-242, 8th European Workshop on Reinforcement Learning
  • MP. Deisenroth, CE. Rasmussen, J. Peters (2008). Model-Based Reinforcement Learning with Continuous States and Actions In: ESANN 2008, (Ed) Verleysen, M. , Advances in Computational Intelligence and Learning: Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008), d-side, Evere, Belgium, 19-24, European Symposium on Artificial Neural Networks
2007
Articles
Contributions to books
  • J. Quiñonero-Candela, CE. Rasmussen, CKI. Williams (2007). Approximation Methods for Gaussian Process Regression In: Large-Scale Kernel Machines, (Ed) Bottou, L. , O. Chapelle, D. DeCoste, J. Weston, MIT Press, Cambridge, MA, USA, 203-223
Talks
2006
Books
Articles
Conference Papers
  • M. Kuss, CE. Rasmussen (2006). Assessing Approximations for Gaussian Process Classification In: Advances in neural information processing systems 18, (Ed) Weiss, Y. , B. Schölkopf, J. Platt, Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, MIT Press, Cambridge, MA, USA, 699-706, ISBN: 0-262-23253-7, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005)
  • D. Görür, F. Jäkel, CE. Rasmussen (2006). A Choice Model with Infinitely Many Latent Features In: ICML 2006, (Ed) Cohen, W. W., A. Moore, Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), ACM Press, New York, NY, USA, 361-368, 23rd International Conference on Machine Learning
  • J. Quinonero Candela, CE. Rasmussen, F. Sinz, O. Bousquet, B. Schölkopf (2006). Evaluating Predictive Uncertainty Challenge In: Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, (Ed) J Quiñonero Candela and I Dagan and B Magnini and F d’Alché-Buc, Machine Learning Challenges: First PASCAL Machine Learning Challenges Workshop (MLCW 2005), Springer, Berlin, Germany, 1-27, ISBN: 978-3-540-33428-6 , First PASCAL Machine Learning Challenges Workshop (MLCW 2005)
Posters
Talks
  • D. Görür, CE. Rasmussen (2006). Sampling for non-conjugate infinite latent feature models (Ed) Bernardo, J. M., 8th Valencia International Meeting on Bayesian Statistics (ISBA 2006)
  • CE. Rasmussen, D. Görür (2006). MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models ICML Workshop on Learning with Nonparametric Bayesian Methods 2006
2005
Articles
Conference Papers
  • CE. Rasmussen, JQ. Candela (2005). Healing the Relevance Vector Machine through Augmentation (Ed) De Raedt, L. , S. Wrobel, Proceedings of the 22nd International Conference on Machine Learning, 689 , ICML 2005
  • J. Quinonero Candela, CE. Rasmussen (2005). Analysis of Some Methods for Reduced Rank Gaussian Process Regression In: Switching and Learning in Feedback Systems, (Ed) Murray Smith, R. , R. Shorten, Springer, Berlin, Germany, 98-127, ISBN: 978-3-540-24457-8 , European Summer School on Multi-Agent Control 2003
Technical Reports
Posters
  • TG. Tanner, NJ. Hill, CE. Rasmussen, FA. Wichmann (2005). Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain (Ed) Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann, 8, MPI for Biological Cybernetics, Tübingen, 109, 8th Tübingen Perception Conference (TWK 2005)
2004
Proceedings
  • CE. Rasmussen, HH. Bülthoff, MA. Giese, B. Schölkopf (2004). Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175 Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), Springer, Berlin, Germany, 581 pages, ISBN: 978-3-540-22945-2, 26th Pattern Recognition Symposium
Conference Papers
  • A. Dubey, S. Hwang, C. Rangel, CE. Rasmussen, Z. Ghahramani, DL. Wild (2004). Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models Pacific Symposium on Biocomputing 2004; Vol. 9, World Scientific Publishing, Singapore, 399-410, Pacific Symposium on Biocomputing 2004
  • F. Sinz, JQ. Candela, G. BakIr, CE. Rasmussen, M. Franz (2004). Learning Depth From Stereo In: 26th DAGM Symposium, (Ed) Rasmussen, C. E., H. H. Bülthoff, B. Schölkopf, M. A. Giese, Pattern Recognition: 26th DAGM Symposium, Springer, Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., Berlin, Germany, 245-252, 26th DAGM Symposium
  • E. Snelson, CE. Rasmussen, Z. Ghahramani (2004). Warped Gaussian Processes In: Advances in Neural Information Processing Systems 16, (Ed) Thrun, S., L.K. Saul, B. Schölkopf, Advances in Neural Information Processing Systems 16, MIT Press, Cambridge, MA, USA, 337-344, ISBN: 0-262-20152-6, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
  • J. Eichhorn, AS. Tolias, A. Zien, M. Kuss, CE. Rasmussen, J. Weston, NK. Logothetis, B. Schölkopf (2004). Prediction on Spike Data Using Kernel Algorithms In: Advances in Neural Information Processing Systems 16, (Ed) S Thrun and LK Saul and B Schölkopf, Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, MIT Press, Cambridge, MA, USA, 1367-1374, ISBN: 0-262-20152-6, 17th Annual Conference on Neural Information Processing Systems (NIPS 2003)
  • MO. Franz, Y. Kwon, CE. Rasmussen, B. Schölkopf (2004). Semi-supervised kernel regression using whitened function classes In: Pattern Recognition, Proceedings of the 26th DAGM Symposium, Lecture Notes in Computer Science, Vol. 3175, (Ed) CE Rasmussen and HH Bülthoff and MA Giese and B Schölkopf, Pattern Recognition, Proceedings of the 26th DAGM Symposium, LNCS 3175, Springer, Berlin, Gerrmany, 18-26, 26th DAGM Symposium
  • J. Kocijan, R. Murray-Smith, CE. Rasmussen, A. Girard (2004). Gasussian process model based predictive control Proceedings of the ACC 2004, 2214-2219, Proceedings of the ACC 2004
  • D. Görür, CE. Rasmussen, AS. Tolias, F. Sinz, NK. Logothetis (2004). Modelling Spikes with Mixtures of Factor Analysers In: Pattern Recognition, (Ed) Rasmussen, C. E. , H.H. Bülthoff, B. Schölkopf, M.A. Giese, Pattern Recognition: Proceedings of the 26th DAGM Symposium, Springer, Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., Berlin, Germany, 391-398, ISBN: 978-3-540-28649-3, 26th DAGM Symposium
  • CE. Rasmussen, M. Kuss (2004). Gaussian Processes in Reinforcement Learning In: Advances in Neural Information Processing Systems 16, (Ed) Thrun, S., L. K. Saul, B. Schölkopf, Advances in Neural Information Processing Systems 16, MIT Press, Cambridge, MA, USA, 751-759, ISBN: 0-262-20152-6, Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Contributions to books
2003
Conference Papers
  • Girard, A. and Rasmussen, CE. and Quiñonero-Candela, J. and Murray-Smith, R. (2003). Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty In: Advances in Neural Information Processing Systems 15, (Ed) Becker, S. , S. Thrun, K. Obermayer, Advances in Neural Information Processing Systems 15, MIT Press, Cambridge, MA, USA, 529-536, ISBN: 0-262-02550-7, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
  • 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: Advances in Neural Information Processing Systems 15, (Ed) Becker, S., S. Thrun and K. Obermayer, Advances in Neural Information Processing Systems 15, MIT Press, Cambridge, MA, USA, 1033-1040, ISBN: 0-262-02550-7, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
  • J. Kocijan, B. Banko, B. Likar, A. Girard, R. Murray-Smith, CE. Rasmussen (2003). A case based comparison of identification with neural network and Gaussian process models. (Ed) Ruano, E.A., Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003, 1, 137-142, Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003
  • J. Kocijan, R. Murray-Smith, CE. Rasmussen, B. Likar (2003). Predictive control with Gaussian process models (Ed) Zajc, B. and M. Tkal, Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool, 352-356, Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool
  • R. Murray-Smith, D. Sbarbaro, CE. Rasmussen, A. Girard (2003). Adaptive, Cautious, Predictive control with Gaussian Process Priors (Ed) Van den Hof, P., B. Wahlberg and S. Weiland, Proceedings of the 13th IFAC Symposium on System Identification, 1195-1200, Proceedings of the 13th IFAC Symposium on System Identification
  • Rasmussen, CE. (2003). Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals (Ed) J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West, Bayesian Statistics 7, 651-659, Bayesian Statistics 7
  • J. Quiñonero-Candela, A. Girard, J. Larsen, CE. Rasmussen (2003). Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting (Ed) Molina, C., T. Adali, J. Larsen, M. Van Hulle, S.C. Douglas and J. Rouat, Proceedings of 2003 IEEE International Workshop on Neural Networks for Signal Processing, 0-0, Proceedings of 2003 IEEE International Workshop on Neural Networks for Signal Processing
  • Rasmussen, CE. and Ghahramani, Z. (2003). Bayesian Monte Carlo In: Advances in Neural Information Processing Systems 15, (Ed) Becker, S. , S. Thrun, K. Obermayer, Advances in Neural Information Processing Systems 15, MIT Press, Cambridge, MA, USA, 489-496, ISBN: 0-262-02550-7, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
  • J. Quiñonero-Candela, A. Girard, J. Larsen, CE. Rasmussen (2003). Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting IEEE International Conference on Acoustics, Speech and Signal Processing, 2, 701-704, IEEE International Conference on Acoustics, Speech and Signal Processing