Poster
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu · Dilin Wang

Mon Dec 5th 06:00 -- 09:30 PM @ Area 5+6+7+8 #149 #None

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state-of-the-art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Stein’s identity and a recently proposed kernelized Stein discrepancy, which is of independent interest.

Author Information

Qiang Liu (Dartmouth College)
Dilin Wang (Dartmouth College)

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