Conference paper

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther; 05) with covariance decoupling techniques (Wipf&Nagarajan; 08, Nickisch&Seeger; 09), it runs at least an order of magnitude faster than the most commonly used EP solver.

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