Fetching the paper…
Reading the bibliography…
Stein Variational Gradient Descent (SVGD) is an important alternative to the Langevin-type algorithms for sampling from probability distributions of the form $\pi(x) \propto \exp(-V(x))$.
A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
C. Stein · 1972
Earlier work this paper cites.
Criteria for recurrence and existence of invariant measures for multidimensional diffusions
R. Bhattacharya · 1978
Earlier work this paper cites.
Bayesian learning via stochastic dynamics
R. Neal · 1992
Earlier work this paper cites.
Representations of knowledge in complex systems
U. Grenander and M. I. Miller · 1994
Earlier work this paper cites.
Exponential convergence of Langevin distributions and their discrete approximations
G. O. Roberts and R. L. Tweedie · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
Earlier work this paper cites.
Penalized regressions: the bridge versus the LASSO
W. J. Fu · 1998
Earlier work this paper cites.
Optimal scaling of discrete approximations to Langevin diffusions
G. O. Roberts and J. S. Rosenthal · 1998
Earlier work this paper cites.
Monte Carlo statistical methods , volume 2
C. P. Robert and G. Casella · 1999
Earlier work this paper cites.
Langevin diffusions and Metropolis-Hastings algorithms
G. O. Roberts and O. Stramer · 2002
Earlier work this paper cites.
Weighted Csiszár-Kullback-Pinsker inequalities and applications to transportation inequalities
F. Bolley and C. Villani · 2005
Earlier work this paper cites.
Elements of information theory
T. M. Cover and J. A. Thomas · 2006
Earlier work this paper cites.
The Bayesian LASSO
T. Park and G. Casella · 2008
Earlier work this paper cites.
Support vector machines
I. Steinwart and A. Christmann · 2008
Earlier work this paper cites.
Ordinary differential equations: an introduction to nonlinear analysis , volume 13
H. Amann · 2011
Earlier work this paper cites.
Strong convergence of an explicit numerical method for SDEs with non-globally Lipschitz continuous coefficients
M. Hutzenthaler, A. Jentzen, and P. E. Kloeden · 2012
Earlier work this paper cites.
Moments and absolute moments of the normal distribution
A. Winkelbauer · 2012
Earlier work this paper cites.
Underwater acoustic noise with generalized Gaussian statistics: Effects on error performance
S. Banerjee and M. Agrawal · 2013
Earlier work this paper cites.
Exploring multi-modal distributions with nested sampling
F. Feroz and J. Skilling · 2013
Earlier work this paper cites.
A note on tamed Euler approximations
S. Sabanis · 2013
Earlier work this paper cites.
A new Bayesian lasso
H. Mallick and N. Yi · 2014
Earlier work this paper cites.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Q. Liu and D. Wang · 2016
Earlier work this paper cites.
A kernelized Stein discrepancy for goodness-of-fit tests
Q. Liu, J. Lee, and M. Jordan · 2016
Earlier work this paper cites.
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
A. Durmus and E. Moulines · 2017
Cited alongside, same era.
Measuring sample quality with kernels
J. Gorham and L. Mackey · 2017
Cited alongside, same era.
Stein variational gradient descent as gradient flow
Q. Liu · 2017
Cited alongside, same era.
Stein variational policy gradient
Y. Liu, P. Ramachandran, Q. Liu, and J. Peng · 2017
Cited alongside, same era.
VAE learning via Stein variational gradient descent
Y. Pu, Z. Gan, R. Henao, C. Li, S. Han, and L. Carin · 2017
Cited alongside, same era.
Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
M. Raginsky, A. Rakhlin, and M. Telgarsky · 2017
Scaling limit of the Stein variational gradient descent: The mean field regime
J. Lu, Y. Lu, and J. Nolen · 2019
Later among the works it cites.
Is there an analog of Nesterov acceleration for MCMC?
Y.-A. Ma, N. Chatterji, X. Cheng, N. Flammarion, P. L. Bartlett, and M. I. Jordan · 2019
Later among the works it cites.
The randomized midpoint method for log-concave sampling
R. Shen and Y. T. Lee · 2019
Later among the works it cites.
Variational annealing of GANs: A Langevin perspective
C. Tao, S. Dai, L. Chen, K. Bai, J. Chen, C. Liu, R. Zhang, G. Bobashev, and L. Carin · 2019
Later among the works it cites.
Rapid convergence of the unadjusted Langevin algorithm: Isoperimetry suffices
S. Vempala and A. Wibisono · 2019
Later among the works it cites.
Stein variational gradient descent with matrix-valued kernels
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Fractional Langevin Monte Carlo: Exploring Lévy driven stochastic differential equations for Markov chain Monte Carlo
U. Şimşekli · 2017
Cited alongside, same era.
On the theory of variance reduction for stochastic gradient Monte Carlo
N. S. Chatterji, N. Flammarion, Y.-A. Ma, P. L. Bartlett, and M. I. Jordan · 2018
Cited alongside, same era.
Convergence of Langevin MCMC in KL-divergence
X. Cheng and P. L. Bartlett · 2018
Cited alongside, same era.
Sharp convergence rates for Langevin dynamics in the nonconvex setting
X. Cheng, N. S. Chatterji, Y. Abbasi-Yadkori, P. L. Bartlett, and M. I. Jordan · 2018
Cited alongside, same era.
A Stein variational Newton method
G. Detommaso, T. Cui, Y. Marzouk, A. Spantini, and R. Scheichl · 2018
Cited alongside, same era.
Efficient Bayesian computation by proximal Markov Chain Monte Carlo: when Langevin meets Moreau
A. Durmus, E. Moulines, and M. Pereyra · 2018
Cited alongside, same era.
D. Wang, Z. Tang, C. Bajaj, and Q. Liu · 2019
Later among the works it cites.
Proximal Langevin algorithm: Rapid convergence under isoperimetry
A. Wibisono · 2019
Later among the works it cites.
Langevin Monte Carlo without smoothness
N. Chatterji, J. Diakonikolas, M. I. Jordan, and P. Bartlett · 2020
Later among the works it cites.
Exponential ergodicity of mirror-Langevin diffusions
S. Chewi, T. Le Gouic, C. Lu, T. Maunu, P. Rigollet, and A. Stromme · 2020
Later among the works it cites.
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets
A. Karagulyan and A. Dalalyan · 2020
Later among the works it cites.
A non-asymptotic analysis for Stein variational gradient descent
A. Korba, A. Salim, M. Arbel, G. Luise, and A. Gretton · 2020
Later among the works it cites.
A stochastic version of Stein variational gradient descent for efficient sampling
L. Li, Y. Li, J.-G. Liu, Z. Liu, and J. Lu · 2020
Later among the works it cites.
Bayesian inference and uncertainty quantification for medical image reconstruction with Poisson data
Q. Zhou, T. Yu, X. Zhang, and J. Li · 2020
Later among the works it cites.
Efficient constrained sampling via the mirror-langevin algorithm
K. Ahn and S. Chewi · 2021
Later among the works it cites.
High-dimensional Bayesian model selection by proximal nested sampling
X. Cai, J. D. McEwen, and M. Pereyra · 2021
Later among the works it cites.
Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev
S. Chewi, M. A. Erdogdu, M. B. Li, R. Shen, and M. Zhang · 2021
Later among the works it cites.
On the convergence of Langevin Monte Carlo: the interplay between tail growth and smoothness
M. A. Erdogdu and R. Hosseinzadeh · 2021
Later among the works it cites.
The Langevin Monte Carlo algorithm in the non-smooth log-concave case
J. Lehec · 2021
Later among the works it cites.
Stein variational gradient descent: many-particle and long-time asymptotics
N. Nüsken and D. Renger · 2021
Later among the works it cites.
Complexity analysis of Stein variational gradient descent under Talagrand’s inequality T1
A. Salim, L. Sun, and P. Richtárik · 2021
Later among the works it cites.
A Feynman-Kac approach for logarithmic Sobolev inequalities
C. Steiner · 2021
Later among the works it cites.
Federated generalized Bayesian learning via distributed Stein variational gradient descent
R. Kassab and O. Simeone · 2022
Closest in time.