论文标题
迈向新的基于跨验证的高斯过程回归的估计器：梯度的有效伴随计算
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients
论文作者
论文摘要
我们考虑通过交叉验证估算高斯过程的协方差函数参数的问题。我们建议使用从评分规则的文献中得出的新的交叉验证标准。我们还提供了一种计算交叉验证标准梯度的有效方法。据我们所知，我们的方法比迄今为止文献中提出的更有效。它使得可以降低共同评估剩余标准及其梯度的复杂性。
We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation. We suggest using new cross-validation criteria derived from the literature of scoring rules. We also provide an efficient method for computing the gradient of a cross-validation criterion. To the best of our knowledge, our method is more efficient than what has been proposed in the literature so far. It makes it possible to lower the complexity of jointly evaluating leave-one-out criteria and their gradients.
