mlsanityData to 14 Oct 2026

Improving Gradient-guided Nested Sampling for Posterior Inference

arXiv 2312.03911 ↗First posted 6 Dec 2023cs.LG stat.CO stat.MEcited by 14

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Abstract (arXiv)

We present a performant, general-purpose gradient-guided nested sampling algorithm, tt GGNS, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows tt GGNS to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 18 of its 69 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

18 of 69 references.

References · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  4. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  5. 2021GFlowNet Foundationscited by 290
  6. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 251
  7. 2021Path Integral Sampler: a stochastic control approach for samplingcited by 181
  8. 2022Flow Annealed Importance Sampling Bootstrapcited by 127
  9. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 114
  10. 2022GFlowNets and variational inferencecited by 106
  11. 2023A theory of continuous generative flow networkscited by 101
  12. 2021Annealed Flow Transport Monte Carlocited by 90
6 more
  1. 2019NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transportcited by 74
  2. 2022Posterior samples of source galaxies in strong gravitational lenses with score-based priorscited by 20
  3. 2017Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculationcited by 15
  4. 2021Proximal nested sampling for high-dimensional Bayesian model selectioncited by 11
  5. 2023Proximal nested sampling with data-driven priors for physical scientistscited by 4
  6. 2016Towards Unifying Hamiltonian Monte Carlo and Slice Samplingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 5; 2026: 3.

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  1. 2024Iterated Denoising Energy Matching for Sampling from Boltzmann Densitiescited by 116
  2. 2024Improved off-policy training of diffusion samplerscited by 48
  3. 2024Adaptive teachers for amortized samplerscited by 23
  4. 2025From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingcited by 19
  5. 2025On scalable and efficient training of diffusion samplerscited by 10
  6. 2024PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimationcited by 8
  7. 2024MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNetscited by 6
  8. 2025Posterior Inference in Latent Space for Scalable Constrained Black-box Optimizationcited by 5
  9. 2025Resonances in reflective Hamiltonian Monte Carlocited by 4
  10. 2024A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applicationscited by 0
  11. 2025Multi-Armed Sampling Problem and the End of Explorationcited by 0
  12. 2026Generative Nested Sampling of Atomistic Thermodynamic Landscapescited by 0
2 more
  1. 2026Quenched Ensemble Samplingcited by 0
  2. 2026Neural Transport Nested Samplingcited by 0