ABC-EP: Expectation Propagation for Likelihoodfree Bayesian Computation

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Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages (2015, methodology): “A natural way to specify f is as code in a probabilistic program.”
Black-box $\alpha$-divergence Minimization (2015, methodology): “Similar approximations have already been applied to EP (Barthelm´e & Chopin, 2011; Gelman et al., 2014; Xu et al., 2014; Teh et al., 2015).”
Learning to Pass Expectation Propagation Messages (2013, background): “The most closely related work is ABC-EP [1].”
Just-In-Time Learning for Fast and Flexible Inference (2014, methodology): “2 can be computed using Monte Carlo methods [2, 7], e.”
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server (2015, methodology): “A number of papers have proposed such an approach, including Barthelmé and Chopin (2011), Heess et al. (2013), Gelman et al. (2014) using importance sampling and Xu et al. (2014), Gelman et al. (2014) using Markov chain Monte Carlo (MCMC).”