摘要翻译：
本文介绍了一种新的回归框架&高斯过程回归网络(GPRN)，它结合了贝叶斯神经网络的结构特性和高斯过程的非参数灵活性。该模型考虑了多个响应变量之间的输入相关信号和噪声相关性、输入相关长度尺度和振幅以及重尾预测分布。我们导出了有效的马尔可夫链、蒙特卡罗和变分贝叶斯推理过程。我们将GPRN作为一个多元输出回归和多元波动模型，在包括1000维基因表达数据集在内的基准数据集上，证明了与八个流行的多输出（多任务）高斯过程模型和三个多元波动模型相比，其性能得到了显著改善。
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英文标题：
《Gaussian Process Regression Networks》
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作者：
Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani
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最新提交年份：
2011
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.
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PDF链接：
https://arxiv.org/pdf/1110.4411