The combination of inducing point methods with stochastic variational
inference has enabled approximate Gaussian Process (GP) inference on large
datasets. Unfortunately, the resulting predictive distributions often exhibit
substantially underestimated uncertainties. Notably, in the regression case the
predictive variance is typically dominated by observation noise, yielding
uncertainty estimates that make little use of the input-dependent function
uncertainty that makes GP priors attractive. In this work we propose two simple
methods for scalable GP regression that address this issue and thus yield
substantially improved predictive uncertainties. The first applies variational
inference to FITC (Fully Independent Training Conditional; Snelson
et.~al.~2006). The second bypasses posterior approximations and instead
directly targets the posterior predictive distribution. In an extensive
empirical comparison with a number of alternative methods for scalable GP
regression, we find that the resulting predictive distributions exhibit
significantly better calibrated uncertainties and higher log likelihoods--often
by as much as half a nat per datapoint.

提出了两种可扩展的高斯过程回归方法，通过应用变分推断和直接处理后验预测分布来改善模型预测不确定性。

参数高斯过程回归器

Parametric Gaussian Process Regressors

Good sparse approximations are essential for practical inference in Gaussian
Processes as the computational cost of exact methods is prohibitive for large
datasets. The Fully Independent Training Conditional (FITC) and the Variational
Free Energy (VFE) approximations are two recent popular methods. Despite
superficial similarities, these approximations have surprisingly different
theoretical properties and behave differently in practice. We thoroughly
investigate the two methods for regression both analytically and through
illustrative examples, and draw conclusions to guide practical application.

本文旨在分析摒弃高代价运算的精确方法所使用的良好稀疏逼近方法，通过理论和实例分析 FITC 和 VFE 方法在回归中的表现并得出了指导实践的结论。