mlsanityData to 7 Oct 2026

Diffusion Generative Modelling for Divide-and-Conquer MCMC

arXiv 2406.11664 ↗First posted 17 Jun 2024stat.ML cs.LG stat.COcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output. An ongoing challenge in the literature is to efficiently perform this merging without imposing distributional assumptions on the posteriors. We propose using diffusion generative modelling to fit density approximations to the subposterior distributions. This approach outperforms existing methods on challenging merging problems, while its computational cost scales more efficiently to high dimensional problems than existing density estimation approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 17 of its 31 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

17 of 31 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC22 Feb
  2. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  4. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 13

  1. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2020Normalization Techniques in Training DNNs: Methodology, Analysis and Applicationcited by 90
  4. 2019Stochastic gradient Markov chain Monte Carlocited by 56
  5. 2015On Markov chain Monte Carlo methods for tall datacited by 50
  6. 2022Compositional Score Modeling for Simulation-based Inferencecited by 46
  7. 2018Deep Energy Estimator Networkscited by 34
  8. 2013Asymptotically Exact, Embarrassingly Parallel MCMCcited by 30
  9. 2024BlackJAX: Composable Bayesian inference in JAXcited by 24
  10. 2016Merging MCMC Subposteriors through Gaussian-Process Approximationscited by 13
  11. 2019Embarrassingly parallel MCMC using deep invertible transformationscited by 11
  12. 2022SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategycited by 6
1 more
  1. 2021Divide-and-Conquer Fusioncited by 3

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Close in wording, no citation either way

  1. 2023Accelerating Markov Chain Monte Carlo sampling with diffusion models
  2. 2022Speed up the inference of diffusion models via shortcut MCMC sampling
  3. 2026Leveraging generative models to assist Monte Carlo sampling
  4. 2025MCMC: Bridging Rendering, Optimization and Generative AI
  5. 2025Accelerated Parallel Tempering via Neural Transports
  6. 2026From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers
  7. 2024Conditional sampling within generative diffusion models
  8. 2022Denoising MCMC for Accelerating Diffusion-Based Generative Models