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Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reproducing kernel Hilbert space.
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A stochastic Newton MCMC method for large-scale statistical inverse problems with application to seismic inversion
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Adaptive-Newton method for explorative learning
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Stein variational gradient descent as gradient flow
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Stein variational policy gradient
Y. Liu, P. Ramachandran, Q. Liu, and J. Peng · 2017
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Riemannian Stein variational gradient descent for Bayesian inference
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
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http://github.com/gianlucadetommaso/Stein-variational-samplers
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Structured Stein variational inference for continuous graphical models
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Analyzing and improving Stein variational gradient descent for high-dimensional marginal inference
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