Fetching the paper…
Reading the bibliography…
Stein Variational Gradient Descent (SVGD) is an algorithm for sampling from a target density which is known up to a multiplicative constant.
Topics in optimal transportation
Villani, C · 2003
Earlier work this paper cites.
Weighted csiszár-kullback-pinsker inequalities and applications to transportation inequalities
Bolley, F. and Villani, C · 2005
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Villani, C · 2008
Earlier work this paper cites.
Vector valued reproducing kernel Hilbert spaces and universality
Carmeli, C., De Vito, E., Toigo, A., and Umanitá, V · 2010
Earlier work this paper cites.
A weak convergence approach to the theory of large deviations , volume 902
Dupuis, P. and Ellis, R. S · 2011
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course , volume 87
Nesterov, Y. E · 2013
Earlier work this paper cites.
A kernel test of goodness of fit
Chwialkowski, K., Strathmann, H., and Gretton, A · 2016
Earlier work this paper cites.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Liu, Q. and Wang, D · 2016
Earlier work this paper cites.
A kernelized Stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J., and Jordan, M · 2016
Earlier work this paper cites.
Theoretical guarantees for approximate sampling from smooth and log-concave densities
Dalalyan, A. S · 2017
Earlier work this paper cites.
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
Durmus, A. and Moulines, E · 2017
Earlier work this paper cites.
Measuring sample quality with kernels
Gorham, J. and Mackey, L · 2017
Earlier work this paper cites.
Stein variational gradient descent as gradient flow
Liu, Q · 2017
Earlier work this paper cites.
Stein variational policy gradient
Liu, Y., Ramachandran, P., Liu, Q., and Peng, J · 2017
Earlier work this paper cites.
VAE learning via Stein variational gradient descent
Pu, Y., Gan, Z., Henao, R., Li, C., Han, S., and Carin, L · 2017
Earlier work this paper cites.
Fractional Langevin Monte Carlo: Exploring Lévy driven stochastic differential equations for Markov Chain Monte Carlo
Şimşekli, U · 2017
Cited alongside, same era.
Langevin Monte Carlo and JKO splitting
Bernton, E · 2018
Cited alongside, same era.
Sampling from a log-concave distribution with projected Langevin Monte Carlo
Bubeck, S., Eldan, R., and Lehec, J · 2018
Cited alongside, same era.
Underdamped Langevin MCMC: A non-asymptotic analysis
Cheng, X., Chatterji, N. S., Bartlett, P. L., and Jordan, M. I · 2018
Cited alongside, same era.
Efficient Bayesian computation by proximal Markov Chain Monte Carlo: when Langevin meets Moreau
Durmus, A., Moulines, E., and Pereyra, M · 2018
Cited alongside, same era.
Mirrored Langevin dynamics
Hsieh, Y.-P., Kavis, A., Rolland, P., and Cevher, V · 2018
Cited alongside, same era.
Rapid convergence of the unadjusted Langevin algorithm: Isoperimetry suffices
Vempala, S. and Wibisono, A · 2019
Later among the works it cites.
Scalable Thompson sampling via optimal transport
Zhang, R., Wen, Z., Chen, C., Fang, C., Yu, T., and Carin, L · 2019
Later among the works it cites.
Sampling from non-log-concave distributions via variance-reduced gradient Langevin dynamics
Zou, D., Xu, P., and Gu, Q · 2019
Later among the works it cites.
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Chewi, S., Gouic, T. L., Lu, C., Maunu, T., and Rigollet, P · 2020
Later among the works it cites.
Stochastic stein discrepancies
Gorham, J., Raj, A., and Mackey, L · 2020
Later among the works it cites.
Federated generalized bayesian learning via distributed stein variational gradient descent
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Wibisono, A · 2018
Cited alongside, same era.
Learning structural weight uncertainty for sequential decision-making
Zhang, R., Li, C., Chen, C., and Carin, L · 2018
Cited alongside, same era.
On the geometry of Stein variational gradient descent
Duncan, A., Nüsken, N., and Szpruch, L · 2019
Cited alongside, same era.
Analysis of Langevin Monte Carlo via convex optimization
Durmus, A., Majewski, S., and Miasojedow, B · 2019
Cited alongside, same era.
Scaling limit of the Stein variational gradient descent: The mean field regime
Lu, J., Lu, Y., and Nolen, J · 2019
Cited alongside, same era.
Is there an analog of Nesterov acceleration for MCMC?
Ma, Y.-A., Chatterji, N., Cheng, X., Flammarion, N., Bartlett, P. L., and Jordan, M. I · 2019
Cited alongside, same era.
Kassab, R. and Simeone, O · 2020
Later among the works it cites.
A non-asymptotic analysis for Stein variational gradient descent
Korba, A., Salim, A., Arbel, M., Luise, G., and Gretton, A · 2020
Later among the works it cites.
Double-loop unadjusted Langevin algorithm
Rolland, P., Eftekhari, A., Kavis, A., and Cevher, V · 2020
Later among the works it cites.
Primal dual interpretation of the proximal stochastic gradient Langevin algorithm
Salim, A. and Richtárik, P · 2020
Later among the works it cites.
The shifted ode method for underdamped langevin mcmc
Foster, J., Lyons, T., and Oberhauser, H · 2021
Closest in time.
Sqrt (d) dimension dependence of langevin monte carlo
Li, R., Zha, H., and Tao, M · 2021
Closest in time.
Stein variational gradient descent: many-particle and long-time asymptotics
Nüsken, N. and Renger, D · 2021
Closest in time.
Sampling with mirrored stein operators
Shi, J., Liu, C., and Mackey, L · 2021
Closest in time.
Balasubramanian, K., Chewi, S., Erdogdu, M. A., Salim, A., and Zhang, M · 2022
Closest in time.