Deep Probabilistic Modelling with Gaussian Processes - Neil D. Lawrence - NIPS Tutorial 2017

Neil Lawrence is a Professor of Machine Learning at the University of Sheffield, but he is currently on leave at Amazon where he is a Director of Machine Learning and founder of Amazon Research Cambridge.

Neural network models are algorithmically simple, but mathematically complex. Gaussian process models are mathematically simple, but algorithmically complex. In this tutorial we will explore Deep Gaussian Process models. They bring advantages in their mathematical simplicity but are challenging in their algorithmic complexity. We will give an overview of Gaussian processes and highlight the algorithmic approximations that allow us to stack Gaussian process models: they are based on variational methods. In the last part of the tutorial will explore a use case exemplar: uncertainty quantification. We end with open questions.

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