Variance Reduction and Quasi-Newton for Particle-Based Variational Inference

ICML 2020 Michael ZhuChang LiuJun Zhu

Particle-based Variational Inference methods (ParVIs), like Stein Variational Gradient Descent, are nonparametric variational inference methods that optimize a set of particles to best approximate a target distribution. ParVIs have been proposed as efficient approximate inference algorithms and as potential alternatives to MCMC methods... (read more)

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