论文标题
可回收的高斯工艺
Recyclable Gaussian Processes
论文作者
论文摘要
我们提出了一个新的框架，用于将独立变化近似值回收到高斯过程。主要的贡献是构建变异集合的构建，鉴于拟合的高斯过程的字典而没有重新审视任何观测值。我们的框架允许回归，分类和异质任务，即连续和离散变量在同一输入域上的混合。我们根据随机过程之间的kullback-leibler差异来利用无限维积分运算符，以重组任意数量的变异稀疏近似值，具有不同的复杂性，似然模型和伪输入的位置。广泛的结果说明了我们在大规模分布实验中的框架的可用性，这也与文献中的确切推论模型相比。
We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a dictionary of fitted Gaussian processes without revisiting any subset of observations. Our framework allows for regression, classification and heterogeneous tasks, i.e. mix of continuous and discrete variables over the same input domain. We exploit infinite-dimensional integral operators based on the Kullback-Leibler divergence between stochastic processes to re-combine arbitrary amounts of variational sparse approximations with different complexity, likelihood model and location of the pseudo-inputs. Extensive results illustrate the usability of our framework in large-scale distributed experiments, also compared with the exact inference models in the literature.
