Fetching the paper…
Reading the bibliography…
Particle-optimization-based sampling (POS) is a recently developed effective sampling technique that interactively updates a set of particles.
Non-asymptotic results for Langevin monte carlo: Coordinate-wise and black-box sampling
Shen, L., Balasubramanian, K., and Ghadimi, S. (2019) · 1902
Earlier work this paper cites.
A class of wasserstein metrics for probability distributions
Givens, C. R. and Shortt, R. M. (1984) · 1984
Earlier work this paper cites.
Diffusion for global optimization in rn
Chiang, T.-S. and Hwang, C.-R. (1987) · 1987
Earlier work this paper cites.
The Fokker-Planck equation
Risken, H. (1989) · 1989
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J. (1992) · 1992
Earlier work this paper cites.
Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise
Mattingly, J. C., Stuartb, A. M., and Higham, D. J. (2002) · 2002
Earlier work this paper cites.
Convergence to equilibrium granular media equations and their euler schemes
Malrieu, F. (2003) · 2003
Earlier work this paper cites.
Weighted Csiszár-Kullback-Pinsker inequalities and applications to transportation inequalities
Bolley, F. and Villani, C. (2005) · 2005
Earlier work this paper cites.
Probabilistic approach for granular media equations in the non-uniformly convex case
Cattiaux, P., Guillin, A., and Malrieu, F. (2008) · 2008
Earlier work this paper cites.
Optimal transport: old and new
Villani, C. (2008) · 2008
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
Welling, M. and Teh, Y. W. (2011) · 2011
Earlier work this paper cites.
Stochastic gradient Hamiltonian Monte Carlo
Chen, T., Fox, E. B., and Guestrin, C. (2014) · 2014
Earlier work this paper cites.
Bayesian sampling using stochastic gradient thermostats
Ding, N., Fang, Y., Babbush, R., Chen, C., Skeel, R. D., and Neven, H. (2014) · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015) · 2015
Earlier work this paper cites.
On the convergence of stochastic gradient MCMC algorithms with high-order integrators
Chen, C., Ding, N., and Carin, L. (2015) · 2015
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. P. (2015) · 2015
Earlier work this paper cites.
Stochastic expectation propagation
Li, Y., Hernández-Lobato, J., and Turner, R. E. (2015) · 2015
Cited alongside, same era.
A complete recipe for stochastic gradient MCMC
Ma, Y. A., Chen, T., and Fox, E. (2015) · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
Cited alongside, same era.
High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P. (2015) · 2015
Cited alongside, same era.
VIME: Variational information maximizing exploration
Houthooft, R., Chen, X., Duan, Y., Schulman, J., De Turck, F., and Abbeel, P. (2016) · 2016
Cited alongside, same era.
Preconditioned stochastic gradient Langevin dynamics for deep neural networks
Li, C., Chen, C., Carlson, D., and Carin, L. (2016) · 2016
Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
Raginsky, M., Rakhlin, A., and Telgarsky, M. (2017) · 2017
Later among the works it cites.
Stein variational message passing for continuous graphical models
Wang, D., Zeng, Z., and Liu, Q. (2017) · 2017
Later among the works it cites.
A hitting time analysis of stochastic gradient Langevin dynamics
Zhang, Y., Liang, P., and Charikar, M. (2017) · 2017
Later among the works it cites.
On the theory of variance reduction for stochastic gradient monte carlo
Chatterji, N. S., Flammarion, N., Ma, Y.-A., Bartlett, P. L., and Jordan, M. I. (2018) · 2018
Closest in time.
A unified particle-optimization framework for scalable Bayesian sampling
Chen, C., Zhang, R., Wang, W., Li, B., and Chen, L. (2018) · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Liu, Q. and Wang, D. (2016) · 2016
Cited alongside, same era.
Structured and efficient variational deep learning with matrix Gaussian posteriors
Louizos, C. and Welling, M. (2016) · 2016
Cited alongside, same era.
Consistency and fluctuations for stochastic gradient Langevin dynamics
Teh, Y. W., Thiery, A. H., and Vollmer, S. J. (2016) · 2016
Cited alongside, same era.
(exploration of the (Non-)asymptotic bias and variance of stochastic gradient Langevin dynamics
Vollmer, S. J., Zygalakis, K. C., and Teh, Y. W. (2016) · 2016
Cited alongside, same era.
Carrillo, J. A., Craig, K., and Patacchini, F. S. (2017) · 2017
Cited alongside, same era.
User-friendly guarantees for the langevin monte carlo with inaccurate gradient
Dalalyan, A. and Karagulyan, A. (2017) · 2017
Cited alongside, same era.
Cheng, X., Chatterji, N. S., Abbasi-Yadkori, Y., Bartlett, P. L., and Jordan, M. I. (2018) · 2018
Closest in time.
Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Şimşekli, U., Liutkus, A., Majewski, S., and Durmus, A. (2018) · 2018
Closest in time.
An Elementary Approach To Uniform In Time Propagation Of Chaos
Durmus, A., Eberle, A., Guillin, A., and Zimmer, R. (2018) · 2018
Closest in time.
An elementary approach to uniform in time propagation of chaos
Durmus, A., Eberle, A., Guillin, A., and Zimmer, R. (2018) · 2018
Closest in time.
Riemannian Stein variational gradient descent for Bayesian inference
Liu, C. and Zhu, J. (2018) · 2018
Closest in time.
Stein variational gradient descent as moment matching
Liu, Q. and Wang, D. (2018) · 2018
Closest in time.
Scaling limit of the Stein variational gradient descent part I: the mean field regime
Lu, J., Lu, Y., and Nolen, J. (2018) · 2018
Closest in time.
Global convergence of Langevin dynamics based algorithms for nonconvex optimization
Xu, P., Chen, J., Zou, D., and Gu, Q. (2018) · 2018
Closest in time.
Message passing stein variational gradient descent
Zhuo, J., Liu, C., Shi, J., Zhu, J., Chen, N., and Zhang, B. (2018) · 2018
Closest in time.
Understanding and accelerating particle-based variational inference
Liu, C., Zhuo, J., Cheng, P., Zhang, R., Zhu, J., and Carin, L. (2019) · 2019
Closest in time.
Scalable thompson sampling via optimal transport
Zhang, R., Wen, Z., Chen, C., and Carin, L. (2019) · 2019
Closest in time.