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Vector Gaussian Processes

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If you have a question about this talk, please contact Ryan Prescott Adams.

Gaussian process regression is often compared to the use of neural networks for function approxmation. One significant difference, however, is that neural networks straightforwardly extend to multiple outputs. Constructing nontrivial (anti)correlations in Gaussian process predictive distributions turns out to be somewhat difficult. I will talk about one way to approach this problem.

This talk is part of the Inference Group series.

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