Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we improve their computational efficiency by using a dual parameterization where each data example is assigned dual parameters, similarly to site parameters used in expectation propagation. Our dual parameterization speeds-up inference using natural gradient descent, and provides a tighter evidence lower bound for hyperparameter learning. The approach has the same memory cost as the current SVGP methods, but it is faster and more accurate.

翻译：Gaussian 偏差过程方法( SVGP) 是非 conjugate Gaussian 过程推论的常见选择, 因为它们的计算效益。 在本文中, 我们通过使用双参数化来提高它们的计算效率, 每个数据示例都配有双重参数, 类似于用于预期传播的站点参数。 我们的双参数化利用自然梯度下降加速推论, 并提供较紧的证据, 用于超参数学习。 这种方法的内存成本与当前 SVGP 方法相同, 但速度更快, 更准确 。