Gaussian process survey

Gaussian process regression

 

Autoregressive GP

Shimizu, Ryoichi. "Entropy maximization principle and selection of the order of an autoregressive Gaussian process." Annals of the Institute of Statistical Mathematics 30.1 (1978): 263-270.

Huang, Shyh-Jier, and Kuang-Rong Shih. "Short-term load forecasting via ARMA model identification including non-Gaussian process considerations." Power Systems, IEEE Transactions on 18.2 (2003): 673-679.

Time series 

N Chapados, Y Bengio. “Augmented Functional Time Series Representation and Forecasting with Gaussian Processes”. Advances in Neural Information Processing Systems, 2007.

S Brahim-Belhouari, A Bermak. “Gaussian process for nonstationary time series prediction”. Computational Statistics and Data Analysis, 2004. 

Rubio, H Pomares, LJ Herrera, I Rojas. “Kernel Methods Applied to Time Series Forecasting“. Lecture Notes in Computer Science, 2007. 

Dynamic model

Y Engel, P Szabo, D Volkinshtein. Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods. Advances in Neural Information Processing Systems, 2006.

J Kocijan, R Murray-Smith, CE Rasmussen, B Likar. Predictive control with Gaussian process models. EUROCON 2003.

J Wang, D Fleet, A Hertzmann . Gaussian Process Dynamical Models. Advances in Neural Information Processing Systems, 2006.

Sparse GP

Lawrence, Neil, Matthias Seeger, and Ralf Herbrich. "Fast sparse Gaussian process methods: The informative vector machine." Advances in neural information processing systems (2003): 625-632.

Quiñonero-Candela, Joaquin, and Carl Edward Rasmussen. "A unifying view of sparse approximate Gaussian process regression." The Journal of Machine Learning Research 6 (2005): 1939-1959.

Snelson, Edward, and Zoubin Ghahramani. "Sparse Gaussian processes using pseudo-inputs." Advances in neural information processing systems 18 (2006): 1257.

Snelson, Edward, and Zoubin Ghahramani. "Local and global sparse Gaussian process approximations." International Conference on Artificial Intelligence and Statistics. 2007.

GPLVM

Lawrence, Neil D. "Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data." Nips. Vol. 2. 2003.

Urtasun, Raquel, David J. Fleet, and Pascal Fua. "3D people tracking with Gaussian process dynamical models." Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 1. IEEE, 2006.

Lawrence, Neil D., and Joaquin Quiñonero-Candela. "Local distance preservation in the GP-LVM through back constraints." Proceedings of the 23rd international conference on Machine learning. ACM, 2006.

Robust GP

Jylänki, Pasi, Jarno Vanhatalo, and Aki Vehtari. "Robust Gaussian process regression with a student-t likelihood." The Journal of Machine Learning Research 12 (2011): 3227-3257.

Mixture Model

Rasmussen, Carl Edward. "The infinite Gaussian mixture model." NIPS. Vol. 12. 1999.

Tresp V. “Mixtures of Gaussian Process”, NIPS 13. 2001

Rasmussen, Carl Edward, and Zoubin Ghahramani. "Infinite mixtures of Gaussian process experts." Advances in neural information processing systems 2 (2002): 881-888. 

Yu, Kai, Volker Tresp, and Shipeng Yu. "A nonparametric hierarchical bayesian framework for information filtering." Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2004.

Kim, Hyoung-Moon, Bani K. Mallick, and C. C. Holmes. "Analyzing nonstationary spatial data using piecewise Gaussian processes." Journal of the American Statistical Association 100.470 (2005): 653-668.

Nonstationary

Paciorek, Christopher J., and Mark J. Schervish. "Nonstationary Covariance Functions for Gaussian Process Regression." NIPS. 2003.

Incorporating Uncertainty 

Jadaliha, Mahdi, et al. "Gaussian process regression for sensor networks under localization uncertainty." environment 3 (2012): 5.

Jadaliha, Mahdi, et al. "Gaussian process regression for sensor networks under localization uncertainty." Signal Processing, IEEE Transactions on 61.2 (2013): 223-237.

S. Choi, M. Jadaliha, J. Choi, and S. Oh. “Distributed Gaussian process regression for mobile sensor network under localization uncertatinty”, CDC 2013 <= 내 논문임