Product Kernel Interpolation for Scalable Gaussian Processes
Product Kernel Interpolation for Scalable Gaussian Processes
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发表时间：
2018-02
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通讯作者：
J. Gardner;Geoff Pleiss;Ruihan Wu;Kilian Q. Weinberger;A. Wilson
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作者：
J. Gardner;Geoff Pleiss;Ruihan Wu;Kilian Q. Weinberger;A. Wilson
最近的工作表明，高斯过程的推理可以有效地使用迭代方法，只依赖于矩阵向量乘法（MVMs）。结构化核插值（SKI）通过使用非常快速的SVM导出近似核来利用这些技术。不幸的是，这种策略受到维度灾难的严重影响。我们开发了一种新的技术MVM的学习，利用产品的内核结构。我们证明，这种技术是广泛适用的，导致线性而不是指数运行时间与SKI的尺寸，以及国家的最先进的渐近复杂性多任务的GP。
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kernel Interpolation (SKI) exploits these techniques by deriving approximate kernels with very fast MVMs. Unfortunately, such strategies suffer badly from the curse of dimensionality. We develop a new technique for MVM based learning that exploits product kernel structure. We demonstrate that this technique is broadly applicable, resulting in linear rather than exponential runtime with dimension for SKI, as well as state-of-the-art asymptotic complexity for multi-task GPs.