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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.
Mathieu Salzmann, Jiancheng Yang, Zheng Dang, Zhen Wei, Haobo Jiang
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Laurent Valentin Jospin, Jesse Ray Murray Lahaye