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Implications of Gaussian process kernel mismatch for out-of-distribution data
Beau Coker · Finale Doshi-Velez
Event URL: https://openreview.net/forum?id=o1woLLxcpv »
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 three solutions --- learning the smoothness using a discrete cosine transform, assuming fatter tails in function-space using a Student-$t$ process, and learning a more flexible kernel using deep kernel learning --- finding some evidence in favor of the first two.

Author Information

Beau Coker (Harvard)
Finale Doshi-Velez (Harvard University)
Finale Doshi-Velez

Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability. Selected Additional Shinies: BECA recipient, AFOSR YIP and NSF CAREER recipient; Sloan Fellow; IEEE AI Top 10 to Watch

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