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About this paper
Vidhi Lalchand, Carl Edward Rasmussen
The paper compares Gaussian process regression with ML-II, treating covariance hyperparameters as uncertain rather than fixing them at a marginal-likelihood optimum. It averages predictions over hyperparameter posteriors approximated by Hamiltonian Monte Carlo or variational inference, using mean-field or full-rank forms. Across UCI datasets, HMC and full-rank VI outperform ML-II except on near-linear Wine; results do not establish universal gains.
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