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Poster
Particle Flow Bayes' Rule
Xinshi Chen · Hanjun Dai · Le Song

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #218
in Posters Tue »

We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists. Its neural parameterization can be trained in a meta-learning framework, allowing this operator to reason about the effect of an individual observation on the posterior, and thus generalize across different priors, observations and to sequential Bayesian inference. We demonstrated the generalization ability of our particle flow Bayes operator in several canonical and high dimensional examples.

Keywords: Approximate Inference · Bayesian Deep Learning · Kernel Methods

Author Information

Xinshi Chen (Georgia Institution of Technology)
Hanjun Dai (Georgia Tech)
Le Song (Georgia Institute of Technology)

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