Practical and scalable probabilistic machine learning

In this talk I will first present an overview of the research challenges we address in my group, in the context of probabilistic machine learning. With this context in mind, I will delve into the details of recent approaches to achieve a practical and scalable way to train deep probabilistic models built on the concept of Gaussian Processes. To this end, I will first briefly introduce what Gaussian Process inference is, then I will move on to describe a model approximation technique, based on random Fourier features, and approximate variational inference to train the resulting model. The talk will conclude with a distributed systems flavour, covering methods and systems to train large scale machine learning models, including data and model parallelism approaches.