mlsanityData to 14 Oct 2026

Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

arXiv 2205.14240 ↗First posted 27 May 2022stat.ML cond-mat.stat-mech cs.LGcited by 10

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Abstract (arXiv)

We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from the current particle positions using a Normalizing Flow (NF), which is differentiable and has good generalization properties in high dimensions. We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence. We show on various examples that the method is competitive against state of the art sampling methods.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 27 May 2022.

Builds on

14 of 44 references.

References · 14

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  2. 2016Density estimation using Real NVPcited by 2,042
  3. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  4. 2016Variational Inference: A Review for Statisticianscited by 1,472
  5. 2017Masked Autoregressive Flow for Density Estimationcited by 684
  6. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 507
  7. 2019Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyrocited by 191
  8. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 148
  9. 2018Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problemcited by 123
  10. 2021Annealed Flow Transport Monte Carlocited by 90
  11. 2020Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimationcited by 88
  12. 2019NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transportcited by 74
2 more
  1. 2016On the Quantitative Analysis of Decoder-Based Generative Modelscited by 40
  2. 2020Sliced Iterative Normalizing Flowscited by 38

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 2; 2026: 1.

10 papers.

  1. 2023On Sampling with Approximate Transport Mapscited by 21
  2. 2026Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?cited by 7
  3. 2025Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimationcited by 6
  4. 2023Learning Rate Free Sampling in Constrained Domainscited by 3
  5. 2023Mixed Variational Flows for Discrete Variablescited by 2
  6. 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Modelscited by 1
  7. 2024Empirical evaluation of normalizing flows in Markov Chain Monte Carlocited by 1
  8. 2023Deterministic Langevin Unconstrained Optimization with Normalizing Flowscited by 0
  9. 2024Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlocited by 0
  10. 2025Reducing normalizing flow complexity for MCMC preconditioningcited by 0