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Journal Article (2)

1.
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)
2.
Journal Article
Kuss, M.; Jäkel, F.; Wichmann, F.: Bayesian inference for psychometric functions. Journal of Vision 5 (5), 8, pp. 478 - 492 (2005)

Conference Paper (3)

3.
Conference Paper
Kuss, M.; Rasmussen, C.: Assessing Approximations for Gaussian Process Classification. In: Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, pp. 699 - 706 (Eds. Weiss, Y.; Schölkopf, B.; Platt, J.). Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005), Whistler, BC, Canada, December 05, 2005 - December 08, 2005. MIT Press, Cambridge, MA, USA (2006)
4.
Conference Paper
Eichhorn, J.; Tolias, A.; Zien, A.; Kuss, M.; Rasmussen, C.; Weston, J.; Logothetis, N.; Schölkopf, B.: Prediction on Spike Data Using Kernel Algorithms. In: Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, pp. 1367 - 1374 (Eds. Thrun, S.; Saul, L.; Schölkopf, B.). Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), Vancouver, BC, Canada, December 08, 2003 - December 13, 2003. MIT Press, Cambridge, MA, USA (2004)
5.
Conference Paper
Rasmussen, C.; Kuss, M.: Gaussian Processes in Reinforcement Learning. In: Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, pp. 751 - 759 (Eds. Thrun, S.; Saul, L.; Schölkopf, B.). Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), Vancouver, BC, Canada, December 08, 2003 - December 13, 2003. MIT Press, Cambridge, MA, USA (2004)

Poster (2)

6.
Poster
Pfingsten, T.; Kuss, M.; Rasmussen, C.: Nonstationary Gaussian Process Regression using a Latent Extension of the Input Space. Eighth World Meeting of the International Society for Bayesian Analysis (ISBA 2006), Benidorm, Spain (2006)
7.
Poster
Kuss, M.; Jäkel, F.; Wichmann, F.: Bayesian Inference for Psychometric Functions. 8th Tübinger Wahrnehmungskonferenz (TWK 2005), Tübingen, Germany (2005)

Thesis - PhD (1)

8.
Thesis - PhD
Kuss, M.: Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning. Dissertation, 205 pp., echnische Universität Darmstadt, Darmstadt, Germany (2006)

Thesis - Diploma (1)

9.
Thesis - Diploma
Kuss, M.: Nonlinear Multivariate Analysis with Geodesic Kernels. Diploma, Technische Universität Berlin, Berlin, Germany (2002)

Report (3)

10.
Report
Kuss, M.; Pfingsten, T.; Csato, L.; Rasmussen, C.: Approximate Inference for Robust Gaussian Process Regression (Technical Report of the Max Planck Institute for Biological Cybernetics, 136). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2005), 27 pp.
11.
Report
Kuss, M.; Jäkel, F.; Wichmann, F.: Approximate Bayesian Inference for Psychometric Functions using MCMC Sampling (Technical Report of the Max Planck Institute for Biological Cybernetics, 135). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2005), 30 pp.
12.
Report
Kuss, M.; Graepel, T.: The Geometry Of Kernel Canonical Correlation Analysis (Technical Report of the Max Planck Institute for Biological Cybernetics, 108). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 11 pp.
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