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Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo.
Projected Stein Variational Gradient Descent
Chen, P. and Ghattas, O. (2020) · 1958
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Measures and Markov processes on function spaces
Baxendale, P. et al. (1976) · 1976
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Global optimization and stochastic differential equations
Aluffi-Pentini, F., Parisi, V., and Zirilli, F. (1985) · 1985
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Diffusions for global optimization
Geman, S. and Hwang, C.-R. (1986) · 1986
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Markov Chain Monte Carlo in Practice
Gilks, W., Richardson, S., and Spiegelhalter, D. (1995) · 1995
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UCI machine learning repository
Asuncion, A. and Newman, D. (2007) · 2007
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Optimization Algorithms on Matrix Manifolds
Absil, P.-A., Mahony, R., and Sepulchre, R. (2008) · 2008
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Radial kernels and their reproducing kernel Hilbert spaces
Scovel, C., Hush, D., Steinwart, I., and Theiler, J. (2010) · 2010
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A Grassmann Manifold Handbook: Basic Geometry and Computational Aspects
Bendokat, T., Zimmermann, R., and Absil, P.-A. (2020) · 2011
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Universality, Characteristic Kernels and RKHS Embedding of Measures
Sriperumbudur, B. K., Fukumizu, K., and Lanckriet, G. R. (2011) · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Welling, M. and Teh, Y. W. (2011) · 2011
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Stochastic equations and differential geometry
Belopolskaya, Y. I. and Dalecky, Y. L. (2012) · 2012
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Energy statistics: A class of statistics based on distances
Székely, G. J. and Rizzo, M. L. (2013) · 2013
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The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Hoffman, M. D., Gelman, A., et al. (2014) · 2014
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S. (2015) · 2015
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A kernel test of goodness of fit
Chwialkowski, K., Strathmann, H., and Gretton, A. (2016) · 2016
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Dimension-independent likelihood-informed MCMC
Cui, T., Law, K. J., and Marzouk, Y. M. (2016) · 2016
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A Kernelized Stein Discrepancy for Goodness-of-fit Tests
Liu, Q., Lee, J., and Jordan, M. (2016) · 2016
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Riemannian Stein variational gradient descent for Bayesian inference
Liu, C. and Zhu, J. (2018) · 2018
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Stein Variational Message Passing for Continuous Graphical Models
Wang, D., Zeng, Z., and Liu, Q. (2018) · 2018
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Bayesian Model-Agnostic Meta-Learning
Yoon, J., Kim, T., Dia, O., Kim, S., Bengio, Y., and Ahn, S. (2018) · 2018
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Message passing Stein variational gradient descent
Zhuo, J., Liu, C., Shi, J., Zhu, J., Chen, N., and Zhang, B. (2018) · 2018
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Hamiltonian Monte Carlo with energy conserving subsampling
Dang, K.-D., Quiroz, M., Kohn, R., Minh-Ngoc, T., and Villani, M. (2019) · 2019
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Generalized Sliced Wasserstein Distances
Kolouri, S., Nadjahi, K., Simsekli, U., Badeau, R., and Rohde, G. (2019) · 2019
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Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
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An Introduction to the Theory of Reproducing Kernel Hilbert Spaces
Paulsen, V. I. and Raghupathi, M. (2016) · 2016
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Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017) · 2017
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Markov chains and mixing times
Levin, D. A. and Peres, Y. (2017) · 2017
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Learning shape trends: parameter estimation in diffusions on shape manifolds
Staneva, V. and Younes, L. (2017) · 2017
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A Stein variational Newton method
Detommaso, G., Cui, T., Marzouk, Y., Spantini, A., and Scheichl, R. (2018) · 2018
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Scaling limit of the Stein variational gradient descent: The mean field regime
Lu, J., Lu, Y., and Nolen, J. (2019) · 2019
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Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models
Wang, D. and Liu, Q. (2019) · 2019
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An introduction to optimization on smooth manifolds
Boumal, N. (2020) · 2020
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Sliced Kernelized Stein Discrepancy
Gong, W., Li, Y., and Hernández-Lobato, J. M. (2021) · 2021
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Sampling with Mirrored Stein Operators
Shi, J., Liu, C., and Mackey, L. (2021) · 2021
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Understanding the Variance Collapse of SVGD in High Dimensions
Ba, J., Erdogdu, M. A., Ghassemi, M., Sun, S., Suzuki, T., Wu, D., and Zhang, T. (2022) · 2022
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