mlsanityData to 7 Oct 2026

Learning to Sample Better

arXiv 2310.11232 ↗First posted 17 Oct 2023cs.LG stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

These lecture notes provide an introduction to recent advances in generative modeling methods based on the dynamical transportation of measures, by means of which samples from a simple base measure are mapped to samples from a target measure of interest. Special emphasis is put on the applications of these methods to Monte-Carlo (MC) sampling techniques, such as importance sampling and Markov Chain Monte-Carlo (MCMC) schemes. In this context, it is shown how the maps can be learned variationally using data generated by MC sampling, and how they can in turn be used to improve such sampling in a positive feedback loop.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 35 references that are in the map. First posted on arXiv 17 Oct 2023.

Builds on

15 of 35 references.

References · 15

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2022Flow Matching for Generative Modelingcited by 5,426
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2018Neural Ordinary Differential Equationscited by 3,733
  7. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  8. 2015Variational Inference with Normalizing Flowscited by 2,161
  9. 2016Density estimation using Real NVPcited by 2,029
  10. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  11. 2022Building Normalizing Flows with Stochastic Interpolantscited by 964
  12. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
3 more
  1. 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
  2. 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
  3. 2021Local-Global MCMC kernels: the best of both worldscited by 21

Built on by

Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2024Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flowscited by 9
  2. 2025On The Hidden Biases of Flow Matching Samplerscited by 2
  3. 2026Flow Map Learning via Nongradient Vector Flowcited by 1
  4. 2026FluxLite: Inference-Time Proposal Control for Discrete Diffusion Modelscited by 1