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Inter-domain Deep Gaussian Processes

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Inter-domain Deep Gaussian Processes (ICML 2020)


Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for modeling data exhibiting global structure but are limited to stationary covariance functions and thus fail to model non-stationary data effectively. We propose Inter-domain Deep Gaussian Processes, an extension of inter-domain shallow GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs), and demonstrate how to leverage existing approximate inference methods to perform simple and scalable approximate inference using inter-domain features in DGPs. We assess the performance of our method on a range of regression tasks and demonstrate that it outperforms inter-domain shallow GPs and conventional DGPs on challenging large-scale real-world datasets exhibiting both global structure as well as a high-degree of non-stationarity.

Keywords: Gaussian Processes, Variational Inference, Bayesian Deep Learning

About the Speaker:

Tim G. J. Rudner is a PhD Candidate in the Department of Computer Science at the University of Oxford, supervised by Yarin Gal and Yee Whye Teh. His research interests span Bayesian deep learning, reinforcement learning, and variational inference. He holds a master’s degree in statistics from the University of Oxford and an undergraduate degree in mathematics and economics from Yale University. Tim is also an AI Fellow at Georgetown University’s Center for Security and Emerging Technology (CSET), a Fellow of the German National Academic Foundation, and a Rhodes Scholar.


This talk is part of the ML@CL Seminar Series with a focus on early career researchers and topics relevant to machine learning and statistics.

This talk is part of the ML@CL Seminar Series series.

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