标题： 每个输入的多个输出样本在单输出高斯过程中的应用 显示英文标题

标题： Multiple output samples per input in a single-output Gaussian process

摘要： 标准高斯过程（GP）仅在训练集中每个输入处考虑一个输出样本。 主观任务的数据集，例如口语语言评估，可能每个输入都有多个人类评分者的输出标签进行标注。 本文提出将GP推广，以允许训练集中存在这些多个输出样本，从而利用可用的输出不确定性信息。 这与多输出GP不同，因为所有输出样本都来自同一任务。 输出密度函数被制定为观察所有输出样本的联合似然，且不重复潜在变量以减少计算成本。 测试集的预测类似于标准GP的推断，区别在于优化的超参数。 这在speechocean762上进行了评估，结果表明它使GP能够计算出与多个人类评分者的参考输出集合更相似的测试集输出分布。 显示英文摘要

摘要： The standard Gaussian Process (GP) only considers a single output sample per input in the training set. Datasets for subjective tasks, such as spoken language assessment, may be annotated with output labels from multiple human raters per input. This paper proposes to generalise the GP to allow for these multiple output samples in the training set, and thus make use of available output uncertainty information. This differs from a multi-output GP, as all output samples are from the same task here. The output density function is formulated to be the joint likelihood of observing all output samples, and latent variables are not repeated to reduce computation cost. The test set predictions are inferred similarly to a standard GP, with a difference being in the optimised hyper-parameters. This is evaluated on speechocean762, showing that it allows the GP to compute a test set output distribution that is more similar to the collection of reference outputs from the multiple human raters.