When utilising PAC-Bayes theory for risk certification, it is usually necessary to estimate and bound the Gibbs risk of the PAC-Bayes posterior. Many works in the literature employ a method for this which requires a large number of passes of the dataset, incurring high computational cost. This manuscript presents a very general alternative which makes computational savings on the order of the dataset size.

翻译：当利用PAC-Bayes理论进行风险认证时,通常有必要估计和约束PAC-Bayes后方人对Gibbs的风险。文献中的许多作品为此采用了一种方法,要求大量通过数据集,从而产生高昂的计算成本。这一手稿提出了一个非常笼统的替代方法,按照数据集大小的顺序计算节省了计算费用。