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Wasserstein Variational Inference
[article]
2018
arXiv
pre-print
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases. The gradients of the Wasserstein variational loss are obtained by backpropagating through the Sinkhorn iterations. This technique results in a very stable likelihood-free training method that can be used with
arXiv:1805.11284v2
fatcat:jqu4g4ldhvchfi2buqhscjnaly