Recent work in scalable approximate Gaussian process regression has discussed a bias-variance-computation trade-off when estimating the log marginal likelihood. We suggest a method that adaptively selects the amount of computation to use when estimating the log marginal likelihood so that the bias of the objective function is guaranteed to be small. While simple in principle, our current implementation of the method is not competitive computationally with existing approximations.

翻译：在估算日志的边际可能性时,我们建议采用一种方法,在估算日志的边际可能性时,适应性地选择要使用的计算量,从而保证目标功能的偏差很小。虽然原则上简单,但我们目前采用的方法与现有的近似值相比并不是竞争性的。