Fetching the paper…
Reading the bibliography…
Langevin diffusion is a commonly used tool for sampling from a given distribution.
Jordan, Richard, David Kinderlehrer, and Felix Otto. "The variational formulation of the Fokker–Planck equation." SIAM journal on mathematical analysis 29.1 (1998): 1-17
1998
Earlier work this paper cites.
Ambrosio, Luigi, Nicola Gigli, and Giuseppe Savaré. Gradient flows: in metric spaces and in the space of probability measures. Springer Science & Business Media, 2008
2008
Earlier work this paper cites.
2015
Earlier work this paper cites.
Bubeck, Sébastien, Ronen Eldan, and Joseph Lehec. "Sampling from a log-concave distribution with Projected Langevin Monte Carlo." Advances in Neural Information Processing Systems 28 (2015)
2015
Earlier work this paper cites.
Danilo Jimenez Rezende and Shakir Mohamed. "Variational inference with normalizing flows" Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 (2015)
2015
Earlier work this paper cites.
Santambrogio, Filippo. "Optimal transport for applied mathematicians." Birkäuser, NY (2015)
2015
Cited alongside, same era.
Dalalyan, Arnak S. "Theoretical guarantees for approximate sampling from smooth and log-concave densities." Journal of the Royal Statistical Society: Series B (Statistical Methodology) (2016)
2016
Cited alongside, same era.
Durmus, Alain, and Eric Moulines. "High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm." (2016)
2016
Cited alongside, same era.
Liu, Qiang, and Dilin Wang. "Stein variational gradient descent: A general purpose bayesian inference algorithm." Advances In Neural Information Processing Systems. 2016
2016
Cited alongside, same era.
2016
Later among the works it cites.
Eberle, Andreas. "Reflection couplings and contraction rates for diffusions." Probability theory and related fields 166.3-4 (2016): 851-886
2016
Later among the works it cites.
Santambrogio, Filippo. "Euclidean, metric, and Wasserstein gradient flows: an overview." Bulletin of Mathematical Sciences 7.1 (2017): 87-154
2017
Closest in time.
2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…