The Gaussian Process Autoregressive Regression Model (GPAR)
The Gaussian Process Autoregressive Regression Model (GPAR)
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DOI：
10.17863/cam.42237
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发表时间：
2018-02
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通讯作者：
James Requeima;Will Tebbutt;W. Bruinsma;Richard E. Turner
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作者：
James Requeima;Will Tebbutt;W. Bruinsma;Richard E. Turner
多输出回归模型必须利用输出之间的依赖关系，以最大限度地提高预测性能。将高斯过程（GP）应用于这种设置通常会产生计算要求高且代表性有限的模型。我们提出了高斯过程自回归回归（GPAR）模型，一个可扩展的多输出GP模型，能够以简单易行的方式捕获非线性的，可能是输入变化的，输出之间的依赖关系：乘积规则用于将输出的联合分布分解为一组条件，每个条件都由标准GP建模。GPAR的功效在各种合成和现实世界的问题上得到了证明，优于现有的GP模型，并在既定的基准上实现了最先进的性能。
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regression (GPAR) model, a scalable multi-output GP model that is able to capture nonlinear, possibly input-varying, dependencies between outputs in a simple and tractable way: the product rule is used to decompose the joint distribution over the outputs into a set of conditionals, each of which is modelled by a standard GP. GPAR's efficacy is demonstrated on a variety of synthetic and real-world problems, outperforming existing GP models and achieving state-of-the-art performance on established benchmarks.