Variable noise and dimensionality reduction for sparse Gaussian processes

Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Volumn , Issue , 2006, Pages 461-468

  Snelson, Edward ^a   Ghahramani, Zoubin ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION METHODS; COMPLEX DATASETS; DIMENSIONALITY REDUCTION; GAUSSIAN PROCESSES; GRADIENT OPTIMIZATION; HIGH DIMENSIONAL DATA; INPUT SPACE; LOW-DIMENSIONAL SPACES; MODELING DATA; NUMBER OF DATUM; REAL-WORLD PROBLEM; REDUCED SPACE; UNCERTAINTY PARAMETERS;

APPROXIMATION THEORY; ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; GAUSSIAN NOISE (ELECTRONIC); OPTIMIZATION;

GAUSSIAN DISTRIBUTION;

EID: 80053178930     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. L. Csató. Sparse online Gaussian processes. Neural Computation, 14:641-668, 2002a.
2. L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis, Aston University, UK, 2002b.
3. M. N. Gibbs. Bayesian Gaussian Processes for Regression and Classification. PhD thesis, Cambridge University, 1997.
4. P. W. Goldberg, C. K. I. Williams, and C. M. Bishop. Regression with input-dependent noise: A Gaussian process treatment. In Advances in Neural Information Processing Systems 10. MIT Press, 1998.
5. D. Higdon, J. Swall, and J. Kern. Non-stationary spatial modeling. In J. M. Bernardo et al., editor, Bayesian Statistics 6. OUP, 1999.
6. N. D. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian process methods: the informative vector machine. In Advances in Neural Information Processing Systems 15. MIT Press, 2003.
7. D. J. C. MacKay. Introduction to Gaussian processes. In C. M. Bishop, editor, Neural Networks and Machine Learning, NATO ASI Series, pages 133-166. Kluwer Academic Press, 1998.
8. C. J. Paciorek and M. J. Schervish. Nonstationary co-variance functions for Gaussian process regression. In Advances in Neural Information Processing Systems 16. MIT Press, 2004.
9. J. Quiñonero Candela. Learning with Uncertainty - Gaussian Processes and Relevance Vector Machines. PhD thesis, Technical University of Denmark, 2004.
10. J. Quiñonero Candela and C. E. Rasmussen. A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6, 2005.
11. C. E. Rasmussen. Evaluation of Gaussian Processes and Other Methods for Non-Linear Regression. PhD thesis, University of Toronto, 1996.
12. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
13. M. Seeger. Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations. PhD thesis, Edinburgh University, 2003.
14. M. Seeger, C. K. I. Williams, and N. D. Lawrence. Fast forward selection to speed up sparse Gaussian process regression. In C. M. Bishop and B. J. Frey, editors, Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.
15. B. W. Silverman. Some aspects of the spline smoothing approach to non-parametric curve fitting. Journal of the Royal Statistical Society, 1985.
16. A. J. Smola and P. Bartlett. Sparse greedy Gaussian process regression. In Advances in Neural Information Processing Systems 13. MIT Press, 2001.
17. E. Snelson and Z. Ghahramani. Sparse Gaussian processes using pseudo-inputs. In Advances in Neural Information Processing Systems 18. MIT Press, 2006.
18. V. Tresp. A Bayesian committee machine. Neural Computation, 12:2719-2741, 2000.
19. F. Vivarelli and C. K. I. Williams. Discovering hidden features with Gaussian processes regression. In Advances in Neural Information Processing Systems 11. MIT Press, 1999.
20. C. K. I. Williams and C. E. Rasmussen. Gaussian processes for regression. In Advances in Neural Information Processing Systems 8. MIT Press, 1996.
21. C. K. I. Williams and M. Seeger. Using the Nyström method to speed up kernel machines. In Advances in Neural Information Processing Systems 13. MIT Press, 2001.