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Particle-based Variational Inference (ParVI) methods approximate the targetdistribution by iteratively evolving finite weighted particle systems. Recentadvances of ParVI methods reveal the benefits of accelerated position updatestrategies and dynamic weight adjustment approaches. In this paper, we proposethe first ParVI framework that possesses both accelerated position update anddynamical weight adjustment simultaneously, named the General AcceleratedDynamic-Weight Particle-based Variational Inference (GAD-PVI) framework.Generally, GAD-PVI simulates the semi-Hamiltonian gradient flow on a novelInformation-Fisher-Rao space, which yields an additional decrease on the localfunctional dissipation. GAD-PVI is compatible with different dissimilarityfunctionals and associated smoothing approaches under three informationmetrics. Experiments on both synthetic and real-world data demonstrate thefaster convergence and reduced approximation error of GAD-PVI methods over thestate-of-the-art.

GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework