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Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest.
A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Stein, Charles · 1972
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Anderes, Ethan and Coram, Marc · 2002
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Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, Peter L and Mendelson, Shahar · 2002
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An introduction to Stein’s method , volume 4
Barbour, Andrew D and Chen, Louis Hsiao Yun · 2005
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Random features for large-scale kernel machines
Rahimi, Ali and Recht, Benjamin · 2007
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Uniform approximation of functions with random bases
Rahimi, Ali and Recht, Benjamin · 2008
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Graphical models, exponential families, and variational inference
Wainwright, Martin J, Jordan, Michael I, et al · 2008
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Probabilistic graphical models: principles and techniques
Koller, Daphne and Friedman, Nir · 2009
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Smoothness, low noise and fast rates
Srebro, Nathan, Sridharan, Karthik, and Tewari, Ambuj · 2010
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Optimal Transport: Theory and Applications , volume 413
Ollivier, Yann, Pajot, Hervé, and Villani, Cédric · 2014
Cited alongside, same era.
A kernel test of goodness-of-fit
Chwialkowski, Kacper, Strathmann, Heiko, and Gretton, Arthur · 2016
Cited alongside, same era.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Liu, Qiang and Wang, Dilin · 2016
Cited alongside, same era.
A kernelized Stein discrepancy for goodness-of-fit tests
Liu, Qiang, Lee, Jason, and Jordan, Michael · 2016
Cited alongside, same era.
Learning to draw samples: With application to amortized MLE for generative adversarial learning
Wang, Dilin and Liu, Qiang · 2016
Reinforcement learning with deep energy-based policies
Haarnoja, Tuomas, Tang, Haoran, Abbeel, Pieter, and Levine, Sergey · 2017
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Stein variational policy gradient
Liu, Yang, Ramachandran, Prajit, Liu, Qiang, and Peng, Jian · 2017
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Control functionals for Monte Carlo integration
Oates, Chris J, Girolami, Mark, and Chopin, Nicolas · 2017
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VAE learning via Stein variational gradient descent
Pu, Yuchen, Gan, Zhe, Henao, Ricardo, Li, Chunyuan, Han, Shaobo, and Carin, Lawrence · 2017
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Bayesian model-agnostic meta-learning
Kim, Taesup, Yoon, Jaesik, Dia, Ousmane, Kim, Sungwoong, Bengio, Yoshua, and Ahn, Sungjin · 2018
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Scaling limit of the stein variational gradient descent part i: the mean field regime
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Cited alongside, same era.
Learning to draw samples with amortized Stein variational gradient descent
Feng, Yihao, Wang, Dilin, and Liu, Qiang · 2017
Cited alongside, same era.
Measuring sample quality with kernels
Gorham, Jackson and Mackey, Lester · 2017
Cited alongside, same era.
Lu, Jianfeng, Lu, Yulong, and Nolen, James · 2018
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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
Zhu, Yinhao and Zabaras, Nicholas · 2018
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