Collaborative multi-output gaussian processes

Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Volumn , Issue , 2014, Pages 643-652

  Nguyen, Trung V ^a   Bonilla, Edwin V ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); OPTIMIZATION;

DEPENDENT TASKS; GAUSSIAN PROCESS; GAUSSIAN PROCESSES; LARGE DATASET; LARGE DATASETS; SPARSITY STRUCTURE; STOCHASTIC OPTIMIZATIONS; VARIATIONAL INFERENCE;

STOCHASTIC MODELS;

EID: 84923281507     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Alvarez, M. and Lawrence, N. D. (2009). Sparse convolved Gaussian processes for multi-output regression. In NIPS.
2. Alvarez, M. A., Luengo, D., Titsias, M. K., and Lawrence, N. D. (2010). Efficient multioutput Gaussian processes through variational inducing kernels. In AISTATS.
3. Bonilla, E. V., Chai, K. M. A., and Williams, C. K. I. (2008). Multi-task Gaussian process prediction. In NIPS.
4. Boyle, P. and Frean, M. (2005). Dependent Gaussian processes. In NIPS.
5. Chai, K. M. A., Williams, C. K., Klanke, S., and Vijayakumar, S. (2008). Multi-task gaussian process learning of robot inverse dynamics. In NIPS.
6. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press.
7. Hensman, J., Fusi, N., and Lawrence, N. D. (2013). Gaussian processes for big data. In Uncertainty in Artificial Intelligence.
8. Hensman, J., Rattray, M., and Lawrence, N. D. (2012). Fast variational inference in the conjugate exponential family. In NIPS.
9. Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37:183-233.
10. Nguyen, T. V. and Bonilla, E. V. (2013). Efficient variational inference for gaussian process regression networks. In AISTATS.
11. Osborne, M. A., Roberts, S. J., Rogers, A., Ramchurn, S. D., and Jennings, N. R. (2008). Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes. In Proceedings of the 7th international conference on Information processing in sensor networks.
12. Quinonero-Candela, J. and Rasmussen, C. E. (2005). A unifying view of sparse approximate Gaussian process regression. The Journal of Machine Learning Research.
13. Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. The MIT Press.
14. Teh, Y. W., Seeger, M., and Jordan, M. I. (2005). Semiparametric latent factor models. In AISTATS.
15. Titsias, M. (2009). Variational learning of inducing variables in sparse Gaussian processes. In AISTATS.
16. Titsias, M. K. and Lázaro-Gredilla, M. (2011). Spike and slab variational inference for multi-task and multiple kernel learning. In NIPS.
17. Vijayakumar, S. and Schaal, S. (2000). Locally weighted projection regression: An O(n) algorithm for incremental real time learning in high dimensional space. In ICML.
18. Wilson, A. G., Knowles, D. A., and Ghahramani, Z. (2012). Gaussian process regression networks. In ICML.