A third-order Langevin MCMC algorithm is proven to sample from smooth log-concave distributions in O(d^(1/4)/epsilon^(1/2)) iterations for generalized linear model potentials, improving on the earlier d^(1/3) barrier.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
A third-order Langevin MCMC algorithm is proven to sample from smooth log-concave distributions in O(d^(1/4)/epsilon^(1/2)) iterations for generalized linear model potentials, improving on the earlier d^(1/3) barrier.