论文标题
通过归一流的流量转换高斯过程
Transforming Gaussian Processes With Normalizing Flows
论文作者
论文摘要
高斯工艺（GPS）可用作灵活的非参数函数先验。受到正常化的工作的不断增长的启发，我们通过可以使输入依赖性的参数可逆转换扩大了这类先验。这样做还可以使我们可以编码可解释的先验知识（例如，有限性约束）。我们得出了与随机变化GP回归一样快的贝叶斯推论问题的变异近似（Hensman等，2013； Dezfouli和Bonilla，2015）。这使该模型成为GP先验其他分层扩展的计算有效替代品（Lazaro-Gredilla，2012; Damianou and Lawrence，2013）。由此产生的算法的计算和推论性能非常出色，我们在一系列数据集上证明了这一点。例如，即使只有5个诱导点和输入依赖性流量，我们的方法也始终具有使用100个诱导点拟合的标准稀疏GP竞争。
Gaussian Processes (GPs) can be used as flexible, non-parametric function priors. Inspired by the growing body of work on Normalizing Flows, we enlarge this class of priors through a parametric invertible transformation that can be made input-dependent. Doing so also allows us to encode interpretable prior knowledge (e.g., boundedness constraints). We derive a variational approximation to the resulting Bayesian inference problem, which is as fast as stochastic variational GP regression (Hensman et al., 2013; Dezfouli and Bonilla,2015). This makes the model a computationally efficient alternative to other hierarchical extensions of GP priors (Lazaro-Gredilla,2012; Damianou and Lawrence, 2013). The resulting algorithm's computational and inferential performance is excellent, and we demonstrate this on a range of data sets. For example, even with only 5 inducing points and an input-dependent flow, our method is consistently competitive with a standard sparse GP fitted using 100 inducing points.
