mlsanityData to 7 Oct 2026

Variational Inference with Hamiltonian Monte Carlo

arXiv 1609.08203 ↗First posted 26 Sep 2016stat.MLcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2016.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 3; 2024: 1; 2025: 2.

17 papers.

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  1. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
  2. 2021MCMC Variational Inference via Uncorrected Hamiltonian Annealingcited by 39
  3. 2022Langevin Diffusion Variational Inferencecited by 26
  4. 2021Nested Variational Inferencecited by 14
  5. 2021Surrogate Likelihoods for Variational Annealed Importance Samplingcited by 14
  6. 2022Bayesian inference via sparse Hamiltonian flowscited by 12
  7. 2022MixFlows: principled variational inference via mixed flowscited by 11
  8. 2022Alleviating Adversarial Attacks on Variational Autoencoders with MCMCcited by 10
  9. 2022Variational Inference with Locally Enhanced Bounds for Hierarchical Modelscited by 4
  10. 2021Interpolating between sampling and variational inference with infinite stochastic mixturescited by 3
  11. 2021Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Methodcited by 2
  12. 2023Reparameterized Variational Rejection Samplingcited by 2
5 more
  1. 2024Differentiable Annealed Importance Sampling Minimizes The Symmetrized Kullback-Leibler Divergence Between Initial and Target Distributioncited by 2
  2. 2022Optimization of Annealed Importance Sampling Hyperparameterscited by 1
  3. 2025Asymptotically exact variational flows via involutive MCMC kernelscited by 1
  4. 2025Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operatorscited by 1
  5. 2023Learning variational autoencoders via MCMC speed measurescited by 0