Stochastic gradient Langevin dynamics with adaptive drifts

DOI10.1080/00949655.2021.1958812OpenAlexW3185641827MaRDI QIDQ3390482

Qifan Song, Se-Hwan Kim, Faming Liang

Publication date: 24 March 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2021.1958812

zbMATH Keywords

adaptive MCMCmomentumdeep neural networkstochastic gradient MCMCmini-batch data

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters ⋮ Self-Supervised Deep Learning for Image Reconstruction: A Langevin Monte Carlo Approach ⋮ Efficient and generalizable tuning strategies for stochastic gradient MCMC ⋮ A stochastic Schumacher diffusion process: probability characteristics computation and statistical analysis

Uses Software

• AdaGrad
• RMSprop
• Adam
• ADADELTA

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Optimization by Simulated Annealing
• The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data
• The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
• ADADELTA
• User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
• Double-parallel Monte Carlo for Bayesian analysis of big data
• Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise.
• (Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Stochastic Approximation in Monte Carlo Computation
• Stochastic Gradient Markov Chain Monte Carlo

This page was built for publication: Stochastic gradient Langevin dynamics with adaptive drifts