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Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.
Weighted sums of certain dependent random variables
Azuma, K · 1967
Earlier work this paper cites.
Probabilistic reasoning in intelligent systems: networks of plausible inference
Pearl, J · 1988
Earlier work this paper cites.
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Aggarwal, C. C., Hinneburg, A., and Keim, D. A · 2001
Earlier work this paper cites.
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Martin, D., Fowlkes, C., Tal, D., and Malik, J · 2001
Earlier work this paper cites.
Expectation propagation for approximate bayesian inference
Minka, T · 2001
Earlier work this paper cites.
Learning with kernels: support vector machines, regularization, optimization, and beyond
Scholkopf, B. and Smola, A. J · 2001
Earlier work this paper cites.
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Sudderth, E., Ihler, A., Freeman, W., and Willsky, A · 2003
Earlier work this paper cites.
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Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
Earlier work this paper cites.
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Minka, T · 2005
Earlier work this paper cites.
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Winn, J. and Bishop, C. M · 2005
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Lan, X., Roth, S., Huttenlocher, D., and Black, M. J · 2006
Earlier work this paper cites.
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Earlier work this paper cites.
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Stein variational gradient descent: A general purpose bayesian inference algorithm
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A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J., and Jordan, M · 2016
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A kernel test of goodness of fit
Chwialkowski, K., Strathmann, H., and Gretton, A · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Q. and Wang, D · 2016
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A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J., and Jordan, M · 2016
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Learning to draw samples with amortized stein variational gradient descent
Feng, Y., Wang, D., and Liu, Q · 2017
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