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Poster
Understanding and Accelerating Particle-Based Variational Inference
Chang Liu · Jingwei Zhuo · Pengyu Cheng · RUIYI (ROY) ZHANG · Jun Zhu

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #232
in Posters Wed »

Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations. We explore ParVIs from the perspective of Wasserstein gradient flows, and make both theoretical and practical contributions. We unify various finite-particle approximations that existing ParVIs use, and recognize that the approximation is essentially a compulsory smoothing treatment, in either of two equivalent forms. This novel understanding reveals the assumptions and relations of existing ParVIs, and also inspires new ParVIs. We propose an acceleration framework and a principled bandwidth-selection method for general ParVIs; these are based on the developed theory and leverage the geometry of the Wasserstein space. Experimental results show the improved convergence by the acceleration framework and enhanced sample accuracy by the bandwidth-selection method.

Keywords: Approximate Inference · Bayesian Methods · Monte Carlo Methods

Author Information

Chang Liu (Tsinghua University)
Jingwei Zhuo (Tsinghua University)
Pengyu Cheng (Duke University)
RUIYI (ROY) ZHANG (Duke University)
Jun Zhu (Tsinghua University)

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