09:00  9:15  Welcome 
09:15  10:15  Gaussian Processes I Have Known 
Tony O'Hagan, Department of Probability and Statistics, University of Sheffield, U.K.  
10:15  11:15  Approximate Methods for GP Regression: A Survey and an Empirical Comparison 
Chris Williams, School of Informatics, University of Edinburgh  
11:15  11:45  Coffee Break 
11:45  12:45  Nonparametric Bayesian Models in Machine Learning 
Kai Yu, Siemens AG, Germany  
12:45  13:45  Lunch 
13:45  14:00 
Some thoughts on Gaussian Processes 
Zoubin Ghahramani, The Gatsby Institute, University College London, U.K.  
14:00  14:45  
Ed Snelson, The Gatsby Institute, University College London, U.K.  
14:45  15:45  Towards bridging the gap between transcriptome and proteome measurement uncertainties with Gaussian processes 
Niranjan, Department of Computer Science, University of Sheffield, U.K.  
15:45  16:15  Coffee Break 
16:15  17:15  Assessing Approximations for Gaussian Process Classification 
Carl Rasmussen, MaxPlanck Institute, Tuebingen, Germany  
Based on joint work with Malte Kuss. The computations required for Gaussian Process Classification are analytically intractable. Several approximation schemes have been proposed recently, but at present it is less than clear how well each of these perform. I compare the Laplace approximation to the Expectation Propagation (EP) algorithm on the evaluation of two key quanteties: the marginal likelihood and the predictive probabilities. I also compare to ground truth, tortuously obtained by Annealed Importance Sampling (MCMC). 
9:00  9:30  KL Corrected Variational Inference for Gaussian Processes 
Neil Lawrence, Department of Computer Science, University of Sheffield, U.K. 

9:30  10:00  Resampling PCA & GP Inference 
Manfred Opper, School of Electronics and Computer Science, University of Southampton, U.K.  
10:00  11:00  Joint Gaussian ProcessDensity Mixtures 
Ole Winther, Technical University of Denmark, Denmark 

Gaussian Processes (GPs) provide a natural framework for Bayesian kernel methods. This talk will be about some work in progress on combining GPs with density estimation in a mixture model. The motivations are: using kernels tuned individually to each mixture component gives a more flexible inputoutput model, unlabelled data can be used in a semisupervised setting and the computational complexity can be reduced because only examples belonging to the same mixture component need to be included in the kernel matrix for that mixture component. A variational Bayes treatment of the joint estimation problem shows how a low complexity solution can be obtained. A more precise approximation to the inference problem of the expectation consistent/propagation type is also possible.  
10:00  11:00  Expectation Consistent Approximate Inference 
Ole Winther, Technical University of Denmark, Denmark 
11:00  11:30  Coffee Break 
11:30  12:30  Requirements for GPC in the Real World 
Anton Schwaighofer, GMD First  
12:30  14:00  Lunch 
14:00  15:00  Sparsity in Gaussian Processes: Questions 
Lehel Csato, MaxPlanck Institute, Tuebingen, Germany  
15:00  16:00  Issues and Challenges in OnLine Gaussian Process Estimation 
Tony Dodd, Automatic Control and Systems Engineering, University of Sheffield, U.K.  
Gaussian processes (GPs) have been successfully applied to a number of wellknown problems in machine learning, signal processing and function approximation. Much of the interest in GPs arises from the multiple interpretations possible: statistical, (Bayesian) probabilistic and reproducing kernel Hilbert spaces (RKHS). Previous research has focused on batch learning for GPs where it is assumed all the data is available. Recently there has been interest in online or sequential learning of GPs. This has applications to incremental solutions for large data set problems, online learning and adaptive nonstationary modelling. Issues and challenges relating to the use of online Gaussian process models in machine learning will be presented. These will include the need to provably guarantee convergence, convergence rates and how to compute the models efficiently. Preliminary results on some of these will be discussed. 
16:00  16:30  Tea 
16:30  17:30  Some concerns about computationally efficient approximations to GPs 
Joaquin Quinonero Candela, MaxPlanck Institute, Tuebingen, Germany  