4 papers · 1 filter
R-ParVI: Particle-based variational inference through lens of rewards
Yongchao Huang
A reward-guided, gradient-free ParVI method, \textit{R-ParVI}, is proposed for sampling partially known densities (e.g. up to a constant). R-ParVI formulates the sampling problem a…
Variational Inference Using Material Point Method
Yongchao Huang
A new gradient-based particle sampling method, MPM-ParVI, based on material point method (MPM), is proposed for variational inference. MPM-ParVI simulates the deformation of a defo…
Variational Inference via Smoothed Particle Hydrodynamics
Yongchao Huang
A new variational inference method, SPH-ParVI, based on smoothed particle hydrodynamics (SPH), is proposed for sampling partially known densities (e.g. up to a constant) or samplin…
Electrostatics-based particle sampling and approximate inference
Yongchao Huang
A new particle-based sampling and approximate inference method, based on electrostatics and Newton mechanics principles, is introduced with theoretical ground, algorithm design and…