It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Rakitsch, Barbara ^a   Lippert, Christoph ^b   Borgwardt, Karsten ^a,d   Stegle, Oliver ^c  

^a Machine Learning and Computational Biology Research Group   (Germany)

^b MICROSOFT RESEARCH   (United States)

^c EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^d UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

CLASSIFICATION MODELS; GAUSSIAN PROCESSES; KRONECKER PRODUCT; PARAMETER INFERENCE; PREDICTION METHODS; SHARING INFORMATION; STRUCTURED NOISE; STRUCTURED RESIDUALS;

FORECASTING;

EID: 84898959873     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Edwin V. Bonilla, Kian Ming Adam Chai, and Christopher K. I.Williams. Multi-task gaussian process prediction. In NIPS, 2007.
2. Mauricio A. Álvarez and Neil D. Lawrence. Sparse convolved gaussian processes for multioutput regression. In NIPS, pages 57-64, 2008.
3. Edwin V. Bonilla, Felix V. Agakov, and Christopher K. I.Williams. Kernel multi-task learning using task-specific features. In AISTATS, 2007.
4. Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, and Maneesh Sahani. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. In NIPS, pages 1881-1888, 2008.
5. Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, and Karsten M. Borgwardt. Efficient inference in matrix-variate gaussian models with iid observation noise. In NIPS, pages 630-638, 2011.
6. Karin Meyer. Estimating variances and covariances for multivariate animal models by restricted maximum likelihood. Genetics Selection Evolution, 23(1):67-83, 1991.
7. V Ducrocq and H Chapuis. Generalizing the use of the canonical transformation for the solution of multivariate mixed model equations. Genetics Selection Evolution, 29(2):205-224, 1997.
8. Hao Zhang. Maximum-likelihood estimation for multivariate spatial linear coregionalization models. Environmetrics, 18(2):125-139, 2007.
9. Andrew Gordon Wilson, David A. Knowles, and Zoubin Ghahramani. Gaussian process regression networks. In ICML, 2012.
10. Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.
11. Alfredo A. Kalaitzis and Neil D. Lawrence. Residual components analysis. In ICML, 2012.
12. Ciyou Zhu, Richard H. Byrd, Peihuang Lu, and Jorge Nocedal. Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Softw., 23(4):550-560, December 1997.
13. Ulrike Ober, Julien F. Ayroles, Eric A. Stone, Stephen Richards, and et al. Using Whole- Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster. PLoS Genetics, 8(5):e1002685+, May 2012.
14. Erin N Smith and Leonid Kruglyak. Gene-environment interaction in yeast gene expression. PLoS Biology, 6(4):e83, 2008.
15. S. Atwell, Y. S. Huang, B. J. Vilhjalmsson,Willems, and et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature, 465(7298):627-631, Jun 2010.