ArticleJournal of statistical computation and simulation2022
Stochastic gradient Langevin dynamics with adaptive drifts.
Article in Journal of statistical computation and simulation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Adaptive Momentum Langevin Dynamics for Efficient Posterior Sampling.Entropy (Basel, Switzerland) · 2026Article
- Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis.Journal of the American Statistical Association · 2026Article
- A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules.Statistica Sinica · 2025Article
- Extended fiducial inference: toward an automated process of statistical inference.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Deep network embedding with dimension selection.Neural networks : the official journal of the International Neural Network Society · 2024Article
- A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We propose a class of adaptive stochastic gradient Markov chain Monte Carlo (SGMCMC) algorithms, where the drift function is adaptively adjusted according to the gradient of past samples to accelerate the convergence of the algorithm in simulations of the distributions with pathological curvatures. We establish the convergence of the proposed algorithms under mild conditions. The numerical examples indicate that the proposed algorithms can significantly outperform the popular SGMCMC algorithms, such as stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian Monte Carlo (SGHMC) and preconditioned SGLD, in both simulation and optimization tasks. In particular, the proposed algorithms can converge quickly for the distributions for which the energy landscape possesses pathological curvatures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.