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   Other publications on Gaussian processes
DLTWM Bowling, D Schuurmans. Gaussian Process Regression for Optimization.

Principal Component Analysis
C Fyfe. Gaussian Processes for Principal Component Analysis.

Approximation Methods
E. Snelson, Z. Ghahramani. Local and global sparse Gaussian process approximations. In AISTATS, 2007.

D Nguyen-Tuong, J Peters. Local Gaussian Process Regression for Real-time Model-based Robot Control. Intelligent Robots and Systems, 2008.

Reinforcement Learning
Y Engel, S Mannor, R Meir. Reinforcement learning with Gaussian processes. Machine Learning-International Workshop Then Conference-, 2005.

Semi-supervised Learning
ND Lawrence, MI Jordan. Semi-supervised learning via Gaussian processes. Advances in Neural Information Processing Systems, 2005.

Relational Learning
W Chu, V Sindhwani, Z Ghahramani, SS Keerthi. Relational Learning with Gaussian Processes. Advances in Neural Information Processing Systems, 2007.

Covariance Functions
M Sugiyama, H Hachiya, C Towell, S Vijayakumar. Geodesic Gaussian Kernels for Value Function Approximation. Proceedings of 2006 Workshop on Information-Based Induction, 2006.

K Teramura, H Okuma, Y Taniguchi, S Makimoto, S Maeda. Gaussian Process Regression for Rendering Music Performance. Proceedings of the 10th International Conference on Music Perception and Cognition, 2008.

Voice Activity Detection
S Park, S Choi. Gaussian Process Regression for Voice Activity Detection and Speech Enhancement. International Joint Conference on Neural Networks, 2008.

Source Separation
S Park, S Choi. Source Separation with Gaussian Process Models. Lecture Notes in Computer Science, 2007.

S Park, S Choi. Gaussian processes for source separation. Acoustics, Speech and Signal Processing, 2008. ICASSP 2008.

Location Estimation
B. Ferris, D. Haehnel, D. Fox. Gaussian Processes for Signal Strength-Based Location Estimation. Robotics: Science and Systems II, 2006.

MP Deisenroth, J Peters, CE Rasmussen. Approximate Dynamic Programming with Gaussian Processes. Proceedings of the 2008 American Control Conference, 2008.

N. Lawrence, M. Seeger, R. Herbrich. Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 625-632. MIT Press, 2003.

C. K. I. Williams, M. Seeger. Using the Nystrom method to speed up kernel machines. In Advances in Neural Information Processing Systems 13, pages 682-688. MIT Press, 2001.

R Martinez-Cantin, N de Freitas, A Doucet, JA. Castellanos. Active Policy Learning for Robot Planning and Exploration under Uncertainty. In Robotics: Science and Systems (RSS), 2007.

    Dr Arman Melkumyan 
Rio Tinto Centre for Mine Automation 
Australian Centre for Field Robotics 
Link Building J13, Room 317 
University of Sydney