论文标题
自动化的增强共轭推断，用于非混合高斯工艺模型
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
论文作者
论文摘要
我们提出了自动化的增强共轭推理，这是一种非缀合水高斯过程（GP）模型的新推断方法。我们的方法会自动构建辅助变量的增强，从而使GP模型有条件地偶联。在增强模型的共轭结构的基础上，我们开发了两种推理方法。首先，使用有效的块坐标上升更新，以封闭形式计算出有效的块坐标上升更新。其次，一个渐近正确的Gibbs采样器，可用于小型数据集。我们的实验表明，与现有的最新黑盒方法相比，我们的方法更快，更健壮的速度更快，更健壮。
We propose automated augmented conjugate inference, a new inference method for non-conjugate Gaussian processes (GP) models. Our method automatically constructs an auxiliary variable augmentation that renders the GP model conditionally conjugate. Building on the conjugate structure of the augmented model, we develop two inference methods. First, a fast and scalable stochastic variational inference method that uses efficient block coordinate ascent updates, which are computed in closed form. Second, an asymptotically correct Gibbs sampler that is useful for small datasets. Our experiments show that our method are up two orders of magnitude faster and more robust than existing state-of-the-art black-box methods.
