Semi-described and semi-supervised learning with Gaussian processes

Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015

Volumn , Issue , 2015, Pages 228-237

  Damianou, Andreas ^a   Lawrence, Neil D ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); ITERATIVE METHODS;

AUTO-REGRESSIVE; EUROPEAN RESEARCH PROJECT; GAUSSIAN PROCESSES; IMPROVE PERFORMANCE; INPUT UNCERTAINTY; PROPAGATION OF UNCERTAINTIES; SEMI- SUPERVISED LEARNING; VARIATIONAL METHODS;

SUPERVISED LEARNING;

EID: 84983156793     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. C. M. Bishop and G. D. James. Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research, A327:580-593, 1993.
2. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semisupervised Learning. MIT Press, Cambridge, MA, 2006.
3. Z. Dai, A. Damianou, J. Hensman, and N. Lawrence. Gaussian process models with parallelization and GPU acceleration. arXiv preprint arXiv:1410.4984, 2014.
4. P. Dallaire, C. Besse, and B. Chaib-Draa. Learning Gaussian process models from uncertain data. In Neural Information Processing, pages 433-440. Springer, 2009.
5. A. Damianou and N. Lawrence. Deep Gaussian processes. In Proceedings of the Sixteenth International Workshop on Artificial Intelligence and Statistics (AISTATS), pages 207-215. JMLR W&CP 31, 2013.
6. A. Damianou and N. Lawrence. Uncertainty propagation in Gaussian process pipelines. NIPS workshop on modern non-parametrics, 2014.
7. A. Damianou, M. Titsias, and N. D. Lawrence. Variational Gaussian process dynamical systems. In Advances in Neural Information Processing Systems 24, pages 2510-2518. 2011.
8. A. Damianou, C. Ek, M. Titsias, and N. Lawrence. Manifold relevance determination. In Proceedings of the 29th International Conference on Machine Learning (ICML), pages 145-152. Omnipress, 2012.
9. M. P. Deisenroth, R. D. Turner, M. F. Huber, U. D. Hanebeck, and C. E. Rasmussen. Robust filtering and smoothing with Gaussian processes. Automatic Control, IEEE Transactions on, 57(7):1865-1871, 2012.
10. M. P. Deisenroth, D. Fox, and C. E. Rasmussen. Gaussian processes for data-efficient learning in robotics and control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 99:1, 2014. ISSN 0162-8828.
11. C. H. Ek, J. Rihan, P. Torr, G. Rogez, and N. D. Lawrence. Ambiguity modeling in latent spaces. In A. Popescu-Belis and R. Stiefelhagen, editors, Machine Learning for Multimodal Interaction (MLMI 2008), LNCS, pages 62-73. Springer-Verlag, 28-30 June 2008.
12. Y. Gal, M. van der Wilk, and C. E. Rasmussen. Distributed variational inference in sparse Gaussian process regression and latent variable models. arXiv:1402.1389, 2014.
13. Z. Ghahramani and M. I. Jordan. Learning from incomplete data. Technical Report CBCL 108, Massachusetts Institute of Technology, 1994.
14. A. Girard, C. E. Rasmussen, J. Quiñonero Candela, and R. Murray-Smith. Gaussian process priors with uncertain inputs-application to multiple-step ahead time series forecasting. In Advances in Neural Information Processing Systems, pages 529-536, 2003.
15. J. J. Hull. A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16:550-554, 1994.
16. D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling. Semi-supervised learning with deep generative models. CoRR, abs/1406.5298, 2014.
17. N. D. Lawrence. The Gaussian process latent variable model. Technical Report CS-06-03, The University of Sheffield, Department of Computer Science, 2006.
18. N. D. Lawrence and M. I. Jordan. Semi-supervised learning via Gaussian processes. In L. Saul, Y. Weiss, and L. Bouttou, editors, Advances in Neural Information Processing Systems, volume 17, pages 753-760, Cambridge, MA, 2005. MIT Press.
19. N. D. Lawrence and J. Quiñonero Candela. Local distance preservation in the GP-LVM through back constraints. In W. Cohen and A. Moore, editors, Proceedings of the International Conference in Machine Learning, volume 23, pages 513-520. Omnipress, 2006. ISBN 1-59593-383-2. doi:10.1145/1143844.1143909.
20. A. McHutchon and C. E. Rasmussen. Gaussian process training with input noise. In NIPS, 2011.
21. J. Oakley and A. O'Hagan. Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika, 89(4):769-784, 2002.
22. M. Osborne and S. J. Roberts. Gaussian processes for prediction. Technical report, Department of Engineering Science, University of Oxford, 2007.
23. J. Quiñonero-Candela. Learning with uncertainty-Gaussian processes and relevance vector machines. PhD thesis, Technical University of Denmark, 2004.
24. J. Quinonero-Cañdela and S. Roweis. Data imputation and robust training with Gaussian processes. NIPS, 2003.
25. J. Quiñonero-Candela, A. Girard, J. Larsen, and C. E. Rasmussen. Propagation of uncertainty in Bayesian kernel models-application to multiple-step ahead forecasting. In Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP'03). 2003 IEEE International Conference on, volume 2, pages II-701. IEEE, 2003.
26. C. Rosenberg, M. Hebert, and H. Schneiderman. Semisupervised self-training of object detection models. In Application of Computer Vision, 2005. WACV/MOTIONS'05 Volume 1., volume 1, pages 29-36, Jan 2005. doi:10.1109/ACVMOT.2005.107.
27. D. B. Rubin. Multiple imputation for nonresponse in surveys, volume 81. John Wiley & Sons, 2004.
28. M. Titsias and N. D. Lawrence. Bayesian Gaussian process latent variable model. Journal of Machine Learning Research - Proceedings Track, 9:844-851, 2010.
29. J. Zhu, A. Ahmed, and E. P. Xing. Medlda: maximum margin supervised topic models. The Journal of Machine Learning Research, 13(1):2237-2278, 2012.