Gaussian Covariance and Scalable Variational Inference
We analyze computational aspects of variational approximate inference techniques for sparse linear models, which have to be understood to allow for large scale applications. Gaussian covariances play a key role, whose approximation is computationally hard. While most previous methods gain scalability by not even representing most posterior dependencies, harmful factorization assumptions can be avoided by employing data-dependent low-rank approximations instead. We provide theoretical and empirical insights into algorithmic and statistical consequences of low-rank covariance approximation errors on decision outcomes in nonlinear sequential Bayesian experimental design.