A hybrid VI-HMC method runs expensive Hamiltonian Monte Carlo only on the neural network parameters most responsible for predictive uncertainty, cutting sampling cost while approximating full HMC posteriors.
Variational Inference with Hamiltonian Monte Carlo
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Variational inference lies at the core of many state-of-the-art algorithms. To improve the approximation of the posterior beyond parametric families, it was proposed to include MCMC steps into the variational lower bound. In this work we explore this idea using steps of the Hamiltonian Monte Carlo (HMC) algorithm, an efficient MCMC method. In particular, we incorporate the acceptance step of the HMC algorithm, guaranteeing asymptotic convergence to the true posterior. Additionally, we introduce some extensions to the HMC algorithm geared towards faster convergence. The theoretical advantages of these modifications are reflected by performance improvements in our experimental results.
citation-role summary
other 1
citation-polarity summary
fields
stat.ML 1years
2025 1verdicts
CONDITIONAL 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators
A hybrid VI-HMC method runs expensive Hamiltonian Monte Carlo only on the neural network parameters most responsible for predictive uncertainty, cutting sampling cost while approximating full HMC posteriors.