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Actually Sparse Variational Gaussian Processes
Jake Cunningham · So Takao · Mark van der Wilk · Marc Deisenroth

In this work we propose a new class of inter-domain variational Gaussian process, constructed by projecting onto a set of compactly supported B-Spline basis functions. Our model is akin to variational Fourier features. However, due to the compact support of the B-Spline basis, we produce sparse covariance matrices. This enables us to make use of sparse linear algebra to efficiently compute matrix operations. After a one-off pre-computation, we show that our method reduces both the memory requirement and the per-iteration computational complexity to linear in the number of inducing points.

Author Information

Jake Cunningham (UCL)
So Takao (University College London)
Mark van der Wilk (Imperial College London)
Marc Deisenroth (University College London)
Marc Deisenroth

Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book [Mathematics for Machine Learning](https://mml-book.github.io) published by Cambridge University Press (2020).

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