Gaussian Process Round Table Programme

The proposed schedule contains talks of 45 minutes length with fifteen minutes of discussion.

  Thursday 9th June  

Coffee & Welcome

9:15 Tony O'Hagan Abstract
10:15 Chris Williams Abstract

Coffee Break

11:45 Kai Yu Abstract


13:45 Zoubin Ghahramani Abstract
14:00 Ed Snelson Abstract


16:15 Carl Rasmussen


17:15 Discussion I  

20:30 Dinner in Monsal Head Hotel

  Friday 10th June  
9:00 Neil Lawrence Abstract
9:30 Manfred Opper Abstract
10:00 Ole Winther Abstract

Coffee Break


Anton Schwaighofer



14:00 Lehel Csato Abstract
15:00 Tony Dodd Abstract


16:30 Joaquin Quinonero Candela Abstract
17:30 Discussion II



Some Suggested Subject Matter for Discussion

If you haven't already decided what you are going to say, Carl and Joaquin have sent a list of issues that they would like to hear about. These are certainly things we should also discuss. If you are not giving a full talk you might like to bring some of your thoughts on some of these issues.

Good applications

Applications for which Gaussian processes are particularly suited, and seem to perform better than other alternative modelling approaches.

Optimization via ML vs cross-validation

Though in regression it seems that optimizing the marginal likelihood leads to good generalization performance, the same cannot be said for classification where at times maximizing the marginal likelihood makes the test error become worse.

Empirical comparisons

the question is whether there exists enough (if any) well designed empirical  comparisons that allow making assessments on the performance of Gaussian Processes compared to competing methods. If not, one may want to motivate the design of good empirical comparisons.

GPs and large scale datasets

what are the most effective means of dealing with large datasets? A number of methods have been proposed, which all seem to suffer from diverse limitations (stability, ability to optimize reduced sets and hyperparameters, quality of the predictive distributions, etc)

Covariance functions:

Stein's book "Interpolation of Spatial Data" claims that "the lengthscales" are not important, only the shape of the covariance function is. The ubiquitous squared exponential covariance function suffers from limitations (ie. it is too smooth). How much effort is yet to be devoted to investigating new covariance functions?

The non-Gaussian Case

In classification, as well as in regression with general noise models, analytic inference is impossible, and use is made of approximations. The number and variety of these is high, and no clear consensus seems to exist on which are better than others.