Search results

Journal Article (10)

1.
Journal Article
Rasmussen, C.; Nickisch, H.: Gaussian Processes for Machine Learning (GPML) Toolbox. The Journal of Machine Learning Research 11, pp. 3011 - 3015 (2010)
2.
Journal Article
Görür, D.; Rasmussen, C.: Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution. Journal of Computer Science and Technology 25 (4), pp. 653 - 664 (2010)
3.
Journal Article
Lázaro-Gredilla, M.; Quiñonero-Candela, J.; Rasmussen, C.; Figueiras-Vidal, A.: Sparse Spectrum Gaussian Process Regression. Journal of Machine Learning Research 11, pp. 1865 - 1881 (2010)
4.
Journal Article
Rasmussen, C.; de la Cruz , B.; Ghahramani, Z.; Wild, D.: 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 (2009)
5.
Journal Article
Deisenroth, M.; Rasmussen, C.; Peters, J.: Gaussian Process Dynamic Programming. Neurocomputing 72 (7-9), pp. 1508 - 1524 (2009)
6.
Journal Article
Nickisch, H.; Rasmussen, C.: Approximations for Binary Gaussian Process Classification. The Journal of Machine Learning Research 9, pp. 2035 - 2078 (2008)
7.
Journal Article
Sonnenburg, S.; Braun, M.; Ong, C.; Bengio, S.; Bottou, L.; Holmes , G.; LeCun, Y.; Müller, K.-R.; Pereira, F.; Rasmussen, C. et al.; Rätsch, G.; Schölkopf, B.; Smola, A.; Vincent, P.; Weston, J.; Williamson, R.: The Need for Open Source Software in Machine Learning. The Journal of Machine Learning Research 8, pp. 2443 - 2466 (2007)
8.
Journal Article
Pfingsten, T.; Herrmann, D.; Rasmussen, C.: Model-based Design Analysis and Yield Optimization. IEEE Transactions on Semiconductor Manufacturing 19 (4), pp. 475 - 486 (2006)
9.
Journal Article
Quinonero Candela, J.; Rasmussen, C.: A Unifying View of Sparse Approximate Gaussian Process Regression. The Journal of Machine Learning Research 6, pp. 1935 - 1959 (2005)
10.
Journal Article
Kuss, M.; Rasmussen, C.: Assessing Approximate Inference for Binary Gaussian Process Classification. The Journal of Machine Learning Research 6, pp. 1679 - 1704 (2005)

Book (1)

11.
Book
Rasmussen, C.; Williams, C.: Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, USA (2006), 248 pp.

Book Chapter (1)

12.
Book Chapter
Quiñonero-Candela, J.; Rasmussen, C.; Williams, C.: Approximation Methods for Gaussian Process Regression. In: Large-Scale Kernel Machines, pp. 203 - 223 (Eds. Bottou, L.; Chapelle, O.; DeCoste, D.; Weston, J.). MIT Press, Cambridge, MA, USA (2007)

Proceedings (1)

13.
Proceedings
Pattern Recognition: 26th DAGM Symposium: Tübingen, Germany, August 30 - September 1, 2004 (Lecture Notes in Computer Science, 3175). 26th Pattern Recognition Symposium, Tübingen, Germany, August 30, 2004 - September 01, 2004. Springer, Berlin, Germany (2004), 581 pp.

Conference Paper (34)

14.
Conference Paper
Duvenaud, D.; Nickisch, H.; Rasmussen, C.: Additive Gaussian Processes. In: Advances in Neural Information Processing Systems 24, pp. 226 - 234 (Eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F.; Weinberger, K.). Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain. Curran, Red Hook, NY, USA (2012)
15.
Conference Paper
Nickisch, H.; Rasmussen, C.: Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. In: DAGM 2010: Pattern Recognition, pp. 271 - 282 (Eds. Goesele, M.; Roth, S.; Kuijper, A.; Schiele, B.; Schindler, K.). 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM 2010), Darmstadt, Germany, September 22, 2010 - September 24, 2010. Springer, Berlin, Germany (2010)
16.
Conference Paper
Saatci, Y.; Turner, R.; Rasmussen, C.: Gaussian process change point models. In: 27th International Conference on Machine Learning (ICML 2010), pp. 927 - 934 (Eds. Fürnkranz, J.; Joachims, T.). 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, June 21, 2010 - June 24, 2010. Omnipress, Madison, WI, USA (2010)
17.
Conference Paper
Turner, R.; Deisenroth, M.; Rasmussen, C.: State-Space Inference and Learning with Gaussian Processes. In: JMLR Workshop and Conference Proceedings, Vol. 9, pp. 868 - 875 (Eds. Teh, Y.; Titterington , M.). Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010), Chia Laguna Resort, Sardinia, Italy, May 13, 2010 - May 15, 2010. JMLR, Madison, WI, USA (2010)
18.
Conference Paper
Rasmussen, C.; Deisenroth, M.: Probabilistic Inference for Fast Learning in Control. In: Recent Advances in Reinforcement Learning: 8th European Workshop, EWRL 2008, Villeneuve d’Ascq, France, June 30-July 3, 2008, pp. 229 - 242 (Eds. Girgin, S.; Loth, M.; Munos, R.; Preux, P.; Ryabko, D.). 8th European Workshop on Reinforcement Learning (EWRL 2008), Villeneuve d‘Ascq, France, June 30, 2008 - July 03, 2008. Springer, Berlin, Germany (2008)
19.
Conference Paper
Deisenroth, M.; Peters, J.; Rasmussen, C.: Approximate Dynamic Programming with Gaussian Processes. In: 2008 American Control Conference, pp. 4480 - 4485. 2008 American Control Conference (ACC 2008), Seattle, WA, USA, June 11, 2008 - June 13, 2008. IEEE Service Center, Piscataway, NJ, USA (2008)
20.
Conference Paper
Deisenroth, M.; Rasmussen, C.; Peters, J.: Model-Based Reinforcement Learning with Continuous States and Actions. In: Advances in computational intelligence and learning: 16th European Symposium on Artificial Neural Networks, pp. 19 - 24 (Ed. Verleysen, M.). 16th European Symposium on Artificial Neural Networks (ESANN 2008), Bruges, Belgium, April 23, 2008 - April 25, 2008. d-side, Evere, Belgium (2008)
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