Computational Aspects of Deep Gaussian Processes in Data Science

我项目的研究领域是数据科学中连续时间/空间模型的研究。特别是，我研究深度高斯过程。由于它具有表示多尺度行为的能力，因此该建模工具的应用非常广泛。这种特性非常重要的例子包括气候建模和图像分析。这样的应用产生了解决“标准”机器学习任务的必要性，例如基于（深度）高斯过程的回归和推理问题。到目前为止，从这样的模型中进行采样在计算上是非常昂贵的。因此，我在这个项目中的目标是改进现有的采样技术，并开发新的策略，以更有效的方式生成样本。在稍后的阶段，我想在贝叶斯推理中使用深度高斯过程先验。为此，我研究了高效的马尔可夫链蒙特卡罗方法。

My project's area of research is the investigation of continuous-time/space models in data science. In particular, I study deep Gaussian processes. Applications for this modelling tool are abundant, thanks to its capacity of representing multi-scale behaviour. Examples where this property is of great importance include climate modelling and image analysis. Such applications give rise to the necessity of solving "standard" machine learning tasks such as regression and inference problems that are based on (deep) Gaussian processes. As of today, sampling from such models is computationally very expensive. My goal in this project is thus to improve existing sampling techniques and to develop new strategies for generating samples in a more efficient way. At a later stage, I would like to employ deep Gaussian process priors in Bayesian inference. To this end, I study efficient Markov chain Monte Carlo methods.