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Gaussian variational approximation with sparse precision matrices
We consider the problem of learning a Gaussian variational approximation to the posterior distribution for a high-dimensional parameter, where we impose sparsity in the precision matrix to reflect appropriate conditional independence structure in the model.
Topic-adjusted visibility metric for scientific articles
In this work, we address the problem of field variation and introduce an article level metric useful for evaluating individual articles' visibility.
Variational inference for sparse spectrum Gaussian process regression
We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation.
Computation
