Towards Improved Learning in Gaussian Processes: The Best of Two Worlds

• URL: http://arxiv.org/abs/2211.06260v1
• Date: Fri, 11 Nov 2022 15:04:10 GMT
• Title: Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
• Authors: Rui Li, ST John, Arno Solin
• Abstract summary: We design a hybrid training procedure where the inference leverages conjugate-computation VI and the learning uses an EP-like marginal likelihood approximation. We empirically demonstrate on binary classification that this provides a good learning objective and generalizes better.
• Score: 18.134776677795077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common choices for approximate inference are Expectation Propagation (EP) and Variational Inference (VI), which have complementary strengths and weaknesses. While VI's lower bound to the marginal likelihood is a suitable objective for inferring the approximate posterior, it does not automatically imply it is a good learning objective for hyperparameter optimization. We design a hybrid training procedure where the inference leverages conjugate-computation VI and the learning uses an EP-like marginal likelihood approximation. We empirically demonstrate on binary classification that this provides a good learning objective and generalizes better.

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