论文标题
四倍的随机高斯流程
Quadruply Stochastic Gaussian Processes
论文作者
论文摘要
我们引入了训练可伸缩的高斯过程（GP）模型的随机变异推理过程，该过程的读取物复杂性与培训点的数量（$ n $）无关，$ n $以及内核近似中使用的数字基函数，$ m $。我们的核心贡献包括对高斯可能性的证据下限（ELBO）的无偏随机估计量，以及一个随机估计器，其随机估计量在其他几种可能性（例如Laplace and Logistic）中降低了Elbo。随机优化的独立性更新$ n $和$ m $的复杂性可以使用大容量的GP模型对巨大数据集进行推断。我们使用GPS和相关向量机对大型分类和回归数据集进行了准确的推断，最多$ M = 10^7 $基础功能。
We introduce a stochastic variational inference procedure for training scalable Gaussian process (GP) models whose per-iteration complexity is independent of both the number of training points, $n$, and the number basis functions used in the kernel approximation, $m$. Our central contributions include an unbiased stochastic estimator of the evidence lower bound (ELBO) for a Gaussian likelihood, as well as a stochastic estimator that lower bounds the ELBO for several other likelihoods such as Laplace and logistic. Independence of the stochastic optimization update complexity on $n$ and $m$ enables inference on huge datasets using large capacity GP models. We demonstrate accurate inference on large classification and regression datasets using GPs and relevance vector machines with up to $m = 10^7$ basis functions.
