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Poster
in
Workshop: Structured Probabilistic Inference and Generative Modeling

Implications of kernel mismatch for OOD data

Beau Coker · Finale Doshi-Velez

Keywords: [ misspecification ] [ Gaussian Processes ] [ kernels ] [ out-of-distribution ]


Abstract:

Gaussian processes provide reliable uncertainty estimates in nonlinear modeling, but a poor choice of the kernel can lead to slow learning. Although learning the hyperparameters of the kernel typically leads to optimal generalization on in-distribution test data, we show that the generalization can be poor on out-of-distribution test data. We then investigate a smoothness learning method, heavier tails, and deep kernel learning as solutions, finding some evidence in favor of the first two.

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