Student-t processes as alternatives to Gaussian processes

Journal of Machine Learning Research

Volumn 33, Issue , 2014, Pages 877-885

  Shah, Amar ^a   Wilson, Andrew Gordon ^a   Ghahramani, Zoubin ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GAUSSIAN NOISE (ELECTRONIC); STUDENTS;

BAYESIAN OPTIMIZATION; CLOSED-FORM EXPRESSION; COMPUTATIONAL COSTS; COVARIANCE STRUCTURES; GAUSSIAN PROCESS MODELS; GAUSSIAN PROCESSES; MARGINAL LIKELIHOOD; PREDICTIVE DISTRIBUTIONS;

GAUSSIAN DISTRIBUTION;

EID: 84954308704     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (23)

1. C. Archambeau and F. Bach. Multiple Gaussian Process Models. Advances in Neural Information Processing Systems, 2010.
2. E. Brochu, M. Cora, and N. de Freitas. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Applications to Active User Modeling and Hierarchical Reinforcement Learning. arXiv, 2010. URL http://arxiv.org/abs/1012.2599.
3. P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis. Modeling Wine Preferences by Data Mining from Physiochemical Properties. Decision Support Systems, Elsevier, 47(4):547-553, 2009.
4. A. P. Dawid. Spherical Matrix Distributions and a Multivariate Model. J. R. Statistical Society B, 39: 254-261, 1977.
5. A. P. Dawid. Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application. Biometrika, 68:265-274, 1981.
6. A. Edelman and N. Raj Rao. Random Matrix Theory. Acta Numerica, 1:1-65, 2005.
7. K. T. Fang, S. Kotz, and K. W. Ng. Symmetric Multi-variate and Related Distributions. Chapman & Hall, 1989.
8. D. R. Jones. A Taxonomy of Global Optimization Methods Based on Response Surfaces. Journal of Global Optimization, 21(4):345-383, 2001.
9. D. Kelker. Distribution Theory of Spherical Distributions and a Location-Scale Parameter. Sankhya, Ser. A, 32:419-430, 1970.
10. R. M. Neal. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification. Technical Report No. 9702, Dept of Statistics, University of Toronto, 1997.
11. R. M. Neal. Slice Sampling. Annals of Statistics, 31(3): 705-767, 2003.
12. R. M. Neal. Handbook of Markov chain Monte Carlo. Chapman & Hall/CRC, 2011.
13. V. Picheny, T. Wagner, and D. Ginsbourger. A Benchmark of Kriging-Based Infill Criteria for Noisy Optimization. Structural and Multidisciplinary Optimization, 48(3):607-626, 2013.
14. C. E. Rasmussen. Evaluation of Gaussian Processes and other Methods for Non-Linear Regression. PhD thesis, Graduate Department of Computer Science, University of Toronto, 1996.
15. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
16. A. Shah, A. G. Wilson, and Z. Ghahramani. Student-t Processes as Alternatives to Gaussian Processes Supplementary Material. 2014. URL http://mlg.eng.cam.ac.uk/amar/tpsupp.pdf.
17. J. Snoek, H. Larochelle, and R. Adams. Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems., 2012.
18. J. Vanhatalo, P. Jylanki, and A. Vehtari. Gaussian Process Regression with Student-t Likelihood. Advances in Neural Information Processing Systems, pages 1910-1918, 2009.
19. A. G. Wilson and R. P. Adams. Gaussian Process Kernels for Pattern Discovery and Extrapolation. Proceedings of the 30th International Conference on Machine Learning, 2013.
20. A. G. Wilson, E. Gilboa, A. Nehorai, and J. P. Cunningham. GPatt: Fast multidimensional pattern extrapolation with Gaussian processes. arXiv preprint arXiv:1310.5288, 2013. URL http://arxiv.org/abs/1310.5288.
21. Z. Xu, F. Yan, and Y. Qi. Sparse Matrix-Variate t Process Blockmodel. Proceedings of the 25th AAAI Conference on Artificial Intelligence, 2011.
22. S. Yu, V. Tresp, and K. Yu. Robust Multi-Task Learning with t-Processes. Proceedings of the 24th International Conference on Machine Learning, 2007.
23. Y. Zhang and D. Y. Yeung. Multi-Task Learning using Generalized t Process. Proceedings of the 13th Conference on Artificial Intelligence and Statistics, 2010.