|
|
 |
|
Publications of Carl Edward Rasmussen
|
|
56 total
result as bibtex
|
|
|
Books (1)
|
Top
|
Rasmussen, C. E. and C. K.I. Williams: Gaussian Processes for Machine Learning. 248, MIT Press, Cambridge, MA, USA (01 2006)
 |
|
|
Proceedings (1)
|
Top
|
Rasmussen, C. E., H. H. Bülthoff, M. A. Giese and B. Schölkopf: Pattern Recognition: 26th DAGM Symposium. Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), 581, Springer, Berlin, Germany (08 2004)
 |
|
|
Journal Articles (10)
|
Top
|
Rasmussen, C. E., B. J. de la Cruz, Z. Ghahramani and D. L. Wild: Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures. IEEE/ACM Transactions on Computational Biology and Bioinformatics 6(4), 615-628 (10 2009)
|
Deisenroth, M. P., C. E. Rasmussen and J. Peters: Gaussian Process Dynamic Programming. Neurocomputing 72(7-9), 1508-1524 (03 2009)
|
Nickisch, H. and C. E. Rasmussen: Approximations for Binary Gaussian Process Classification. Journal of Machine Learning Research 9, 2035-2078 (10 2008)
|
Sonnenburg, S., M. L. Braun, C. S. Ong, S. Bengio, L. Bottou, G. Holmes, Y. LeCun, K.-R. Müller, F. Pereira, C. E. Rasmussen, G. Rätsch, B. Schölkopf, A. Smola, P. Vincent, J. Weston and R. C. Williamson: The Need for Open Source Software in Machine Learning. Journal of Machine Learning Research 8, 2443-2466 (10 2007)
|
Pfingsten, T., D. Herrmann and C. E. Rasmussen: Model-based Design Analysis and Yield Optimization. IEEE Transactions on Semiconductor Manufacturing 19(4), 475-486 (02 2006)
 |
Quiñonero Candela, J. and C. E. Rasmussen: A Unifying View of Sparse Approximate Gaussian Process Regression. Journal of Machine Learning Research 6, 1935-1959 (12 2005)
|
Kuss, M. and C. Rasmussen: Assessing Approximate Inference for Binary Gaussian Process Classification. Journal of Machine Learning Research 6, 1679 - 1704 (10 2005)
|
Andersen, I. K, A. Szymkowiak, C. E. Rasmussen, L. G. Hanson, J. R. Marstrand, H. B. W. Larsson and L. K. Hansen: Perfusion Quantification using Gaussian Process Deconvolution. Magnetic Resonance in Medicine (48), 351-361, Wiley (2002)
|
Hansen, L. K. and C. E. Rasmussen: Pruning from Adaptive Regularization. Neural Computation 6(6), 1222-1231, MIT Press (1994)
|
Rasmussen, C. E. and D. J. Willshaw: Presynaptic and Postsynaptic Competition in models for the Development of Neuromuscular Connections. Biological Cybernetics 68, 409-419 (1993)
|
|
|
Conference Papers (31)
|
Top
|
Rasmussen, C. E. and M. P. Deisenroth: Probabilistic Inference for Fast Learning in Control. Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008), 229-242. (Eds.) Girgin, S., M. Loth, R. Munos, P. Preux, D. Ryabko, Springer, Berlin, Germany (11 2008)
|
Deisenroth, M. P., J. Peters and C. E. Rasmussen: Approximate Dynamic Programming with Gaussian Processes. Proceedings of the 2008 American Control Conference (ACC 2008), 4480-4485, IEEE Service Center, Piscataway, NJ, USA (06 2008)
|
Deisenroth, M. P., C. E. Rasmussen and J. Peters: Model-Based Reinforcement Learning with Continuous States and Actions. Advances in Computational Intelligence and Learning: Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008), 19-24. (Eds.) Verleysen, M. d-side, Evere, Belgium (04 2008)
|
Görür, D., F. Jäkel and C. E. Rasmussen: A Choice Model with Infinitely Many Latent Features. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 361-368. (Eds.) Cohen, W. W., A. Moore, ACM Press, New York, NY, USA (06 2006)
|
Kuss, M. and C. E. Rasmussen: Assessing Approximations for Gaussian Process Classification. Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, 699-706. (Eds.) Weiss, Y., B. Schölkopf, J. Platt, MIT Press, Cambridge, MA, USA (05 2006)
|
Quiñonero Candela, J., C. E. Rasmussen, F. Sinz, O. Bousquet and B. Schölkopf: Evaluating Predictive Uncertainty Challenge. Machine Learning Challenges: First PASCAL Machine Learning Challenges Workshop (MLCW 2005), 1-27. (Eds.) Quiñonero Candela, J., I. Dagan, B. Magnini, F. d’Alché-Buc, Springer, Berlin, Germany (04 2006)
|
Rasmussen, C. E. and J. Quinonero Candela: Healing the Relevance Vector Machine through Augmentation. Proceedings of the 22nd International Conference on Machine Learning, 689 - 696. (Eds.) De Raedt, L., S. Wrobel (2005)
|
Görür, D., C. E. Rasmussen, A. S. Tolias, F. Sinz and N. K. Logothetis: Modelling Spikes with Mixtures of Factor Analysers. Pattern Recognition: Proceedings of the 26th DAGM Symposium, 391-398. (Eds.) Rasmussen, C. E., H. H. Bülthoff, B. Schölkopf, M. A. Giese, Springer, Berlin, Germany (09 2004)
|
Sinz, F., J. Quinonero Candela, G. BakIr, C. E. Rasmussen and M. Franz: Learning Depth From Stereo. Pattern Recognition: 26th DAGM Symposium, 245-252. (Eds.) Rasmussen, C. E., H. H. Bülthoff, B. Schölkopf, M. A. Giese, Springer, Berlin, Germany (09 2004)
|
Eichhorn, J., A. S. Tolias, A. Zien, M. Kuss, C. E. Rasmussen, J. Weston, N. K. Logothetis and B. Schölkopf: Prediction on Spike Data Using Kernel Algorithms. Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference 16, 1367-1374. (Eds.) Thrun, S., L. K. Saul, B. Schölkopf, MIT Press, Cambridge, MA, USA (06 2004)
|
Dubey, A., S. Hwang, C. Rangel, C. E. Rasmussen, Z. Ghahramani and D. L. Wild: Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models. Pacific Symposium on Biocomputing 2004; Vol. 9, 399-410, World Scientific Publishing, Singapore (2004)
|
Franz, M.O., Y. Kwon, C. E. Rasmussen and B. Schölkopf: Semi-supervised kernel regression using whitened function classes. Pattern Recognition, Proceedings of the 26th DAGM Symposium LNCS 3175, 18-26. (Eds.) Rasmussen, C. E., H. H. Bülthoff, M. A. Giese and B. Schölkopf, Springer, Berlin, Germany (2004)
|
Kocijan, J., R. Murray-Smith, C. E. Rasmussen and A. Girard: Gasussian process model based predictive control. Proceedings of the ACC 2004, 2214-2219 (2004)
|
Rasmussen, C. E. and M. Kuss: Gaussian Processes in Reinforcement Learning. Advances in Neural Information Processing Systems 16, 751-759. (Eds.) Thrun, S., L. K. Saul and B. Schölkopf, MIT Press (2004)
|
Snelson, E., C. E. Rasmussen and Z. Ghahramani: Warped Gaussian Processes. Advances in Neural Information Processing Systems 16, 337-344. (Eds.) Thrun, S., L. Saul and B. Schölkopf, MIT Press, Cambridge, MA (2004)
|
Murray-Smith, R., D. Sbarbaro, C.E. Rasmussen and A. Girard: Adaptive, Cautious, Predictive control with Gaussian Process Priors. Proceedings of the 13th IFAC Symposium on System Identification, 1195-1200. (Eds.) Van den Hof, P., B. Wahlberg and S. Weiland, Elsevier Science Ltd, Oxford, UK (August 2003)
|
Kocijan, J., B. Banko, B. Likar, A. Girard, R. Murray-Smith and C. E. Rasmussen: A case based comparison of identification with neural network and Gaussian process models. Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003 1, 137-142. (Eds.) Ruano, E.A. (April 2003)
|
Beal, M. J., Z. Ghahramani and C. E. Rasmussen: The Infinite Hidden Markov Model. Advances in Neural Information Processing Systems 14, 577-584. (Eds.) T. Dietterich, S. Becker, Z. Ghahramani, MIT Press (2003)
|
Girard, A., C. E. Rasmussen, J. Quiñonero-Candela and R. Murray-Smith: Multiple-step ahead prediction for non linear dynamic systems -- A Gaussian Process treatment with propagation of the uncertainty. Advances in Neural Information Processing Systems 15, 529-536. (Eds.) Suzanna Becker, Sebastian Thrun and Klaus Obermayer, MIT Press, Cambridge, Massachussetts (2003)
|
Kocijan, J., R. Murray-Smith, C. E. Rasmussen and B. Likar: Predictive control with Gaussian process models. Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool, 352-356. (Eds.) Zajc, B. and M. Tkal, IEEE, Piscataway (2003)
|
Quiñonero-Candela, J., A. Girard, J. Larsen and C.E. Rasmussen: Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting. Proceedings of 2003 IEEE International Workshop on Neural Networks for Signal Processing. (Eds.) Molina, C., T. Adali, J. Larsen, M. Van Hulle, S.C. Douglas and J. Rouat, IEEE Press, Piscataway, New Jersey (2003) [Note: electronical version]
|
Quiñonero-Candela, J., A. Girard, J. Larsen and C.E. Rasmussen: 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 (2003)
|
Rasmussen, C. E.: Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals. Bayesian Statistics 7, 651-659. (Eds.) J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West, Oxford University Press (2003)
|
Rasmussen, C. E. and Z. Ghahramani: Bayesian Monte Carlo. Advances in Neural Information Processing Systems 15, 489-496. (Eds.) Suzanna Becker, Sebastian Thrun and Klaus Obermayer, MIT Press (2003)
|
Solak, E., R. Murray-Smith, W. E. Leithead, D. Leith and C. E. Rasmussen: Derivative observations in Gaussian Process models of dynamic systems. Advances in Neural Information Processing Systems 15, 1033-1040. (Eds.) Becker, S., S. Thrun and K. Obermayer, MIT Press (2003)
|
Rasmussen, C. E. and Z. Ghahramani: Infinite Mixtures of Gaussian Process Experts. (Eds.) Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin (2002)
|
Rasmussen, C. E. and Z. Ghahramani: Occam's Razor. Advances in Neural Information Processing Systems 13, 294-300. (Eds.) Todd Leen, Thomas G. Dietterich and Volker Tresp, MIT Press (2001)
|
Højen-Sørensen, P. A. d. F. R., C. E. Rasmussen and L. K. Hansen: Bayesian modelling of fMRI time series. 754-760. (Eds.) Sara A. Solla, Todd K. Leen and Klaus-Robert Müller, MIT Press (2000)
|
Rasmussen, C. E.: The Infinite Gaussian Mixture Model. Advances in Neural Information Processing Systems 12, 554-560. (Eds.) Solla, S. A., T. K. Leen and K. R. Müller, MIT Press (2000)
|
Rasmussen, C. E.: A practical Monte Carlo implementation of Bayesian learning. Advances in Neural Processing Systems 8, 598-604. (Eds.) Touretzky, D. S., M. C. Mozer and M. E. Hasselmo, MIT Press (1996)
|
Williams, C. K. I. and C. E. Rasmussen: Gaussian Processes for Regression. Advances in Neural Processing Systems 8, 598-604. (Eds.) D. S. Touretzky, M. C. Mozer, M. E. Hasselmo, MIT Press (1996)
|
|
|
Book Chapters (3)
|
Top
|
Quinonero-Candela, J., C. E. Rasmussen and C. K.I. Williams: Approximation Methods for Gaussian Process Regression. Large-Scale Kernel Machines, 203-223. (Eds.) Bottou, L., O. Chapelle, D. DeCoste, J. Weston, MIT Press, Cambridge, MA, USA (09 2007)
|
Quinonero Candela, J. and C. E. Rasmussen: Analysis of Some Methods for Reduced Rank Gaussian Process Regression. Switching and Learning in Feedback Systems, 98-127. (Eds.) Murray Smith, R., R. Shorten, Springer, Berlin, Heidelberg (2005)
|
Rasmussen, C. E.: Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003, Revised Lectures 3176, 63-71. (Eds.) Bousquet, O., U. von Luxburg and G. Rätsch, Springer-Verlag, Heidelberg (2004) [Note: Copyright by Springer]
|
|
|
MPI-Technical Reports (1)
|
Top
|
Kuss, M., T. Pfingsten, L. Csato and C. E. Rasmussen: Approximate Inference for Robust Gaussian Process Regression. (136), Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2005)
|
|
|
Technical Reports (3)
|
Top
|
Quiñonero-Candela, J, Agathe Girard and C. E. Rasmussen: Prediction at an Uncertain Input for Gaussian Processes and Relevance Vector Machines - Application to Multiple-Step Ahead Time-Series Forecasting. Technical report No.(IMM-2003-18), Technical University of Denmark (2003)
|
Williams, C. K. I, C. E. Rasmussen, A. Scwaighofer and V. Tresp: Observations on the Nyström Method for Gaussian Process Prediction. Technical report (2002)
|
Rasmussen, C. E., R. M. Neal, G. E. Hinton, D. van Camp, M. Revow, Z. Ghahramani, R. Kustra and R. Tibshirani: The DELVE user manual. Technical report (12 1996)
|
|
|
Abstracts (2)
|
Top
|
Pfingsten, T., M. Kuss and C. E. Rasmussen: Nonstationary Gaussian Process Regression using a Latent Extension of the Input Space. 3 (not published) (01/01/ 2006)
|
T.G. Tanner, N.J. Hill, C. E. Rasmussen and F.A. Wichmann: Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain. 109. (Eds.) Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann, Knirsch Verlag, Kirchentellisfurt (Jan 2005)
|
|
|
PhD Theses (1)
|
Top
|
Rasmussen, C. E.: Evaluation of Gaussian Processes and other Methods for Non-Linear Regression. (1996)
|
|
|
Talks (3)
|
Top
|
Görür, D. and C. E. Rasmussen: Dirichlet Process Mixtures of Factor Analysers. Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), Valencia, Spain (06 2007)
|
Görür, D. and C. E. Rasmussen: Sampling for non-conjugate infinite latent feature models. (Eds.) Bernardo, J. M. 8th Valencia International Meeting on Bayesian Statistics (ISBA 2006), Benidorm, Spain (06 2006)
|
Rasmussen, C. E. and D. Görür: MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models. ICML Workshop on Learning with Nonparametric Bayesian Methods 2006, Pittsburgh, PA, USA (06 2006)
|
|
| |
|
|
|
