@report {1617113,
	title = {Worst-Case Bounds for Gaussian Process Models},
	year = {2006},
	address = {Advances in Neural Information Processing Systems 18 (NIPS 2005)},
	abstract = {We present a competitive analysis of some non-parametric Bayesian al- gorithms in a worst-case online learning setting, where no probabilistic assumptions about the generation of the data are made. We consider models which use a Gaussian process prior (over the space of all func- tions) and provide bounds on the regret (under the log loss) for com- monly used non-parametric Bayesian algorithms {\textemdash} including Gaussian regression and logistic regression {\textemdash} which show how these algorithms can perform favorably under rather general conditions. These bounds ex- plicitly handle the infinite dimensionality of these non-parametric classes in a natural way. We also make formal connections to the minimax and minimum description length (MDL) framework. Here, we show precisely how Bayesian Gaussian regression is a minimax strategy.},
	url = {doiURL},
	author = {Sham M. Kakade and Matthias W. Seeger and Dean P. Foster}
}
