Title: GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models Show Chinese title

Title: GP-ALPS：多输出高斯过程模型的自动潜在过程选择

Abstract: A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosing the number of latent processes and their kernels. These choices are typically done manually, which can be time consuming and prone to human biases. We propose Gaussian Process Automatic Latent Process Selection (GP-ALPS), which automatically chooses the latent processes by turning off those that do not meaningfully contribute to explaining the data. We develop a variational inference scheme, assess the quality of the variational posterior by comparing it against the gold standard MCMC, and demonstrate the suitability of GP-ALPS in a set of preliminary experiments. Show Chinese abstract

Abstract: 一种简单且广泛采用的方法是将高斯过程（GPs）扩展到多个输出，即每个输出都被建模为一组共享的、未观测到的潜在GPs的线性组合。 这种方法的一个问题是选择潜在过程的数量及其核函数。 这些选择通常手动进行，这可能耗时且容易产生人为偏差。 我们提出了高斯过程自动潜在过程选择（GP-ALPS），该方法通过关闭那些对解释数据没有显著贡献的潜在过程来自动选择潜在过程。 我们开发了一种变分推断方案，通过将其与黄金标准MCMC进行比较来评估变分后验的质量，并在一组初步实验中展示了GP-ALPS的适用性。