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Poster
Understanding Probabilistic Sparse Gaussian Process Approximations
Matthias Bauer · Mark van der Wilk · Carl Edward Rasmussen

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #32 #None

Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.

Author Information

Matthias Bauer (University of Cambridge)
Mark van der Wilk (University of Cambridge)
Carl Edward Rasmussen (University of Cambridge)

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