mlsanityData to 14 Oct 2026

Projected Stein Variational Gradient Descent

arXiv 2002.03469 ↗First posted 9 Feb 2020cs.LG stat.MLcited by 33

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Abstract (arXiv)

The curse of dimensionality is a longstanding challenge in Bayesian inference in high dimensions. In this work, we propose a projected Stein variational gradient descent (pSVGD) method to overcome this challenge by exploiting the fundamental property of intrinsic low dimensionality of the data informed subspace stemming from ill-posedness of such problems. We adaptively construct the subspace using a gradient information matrix of the log-likelihood, and apply pSVGD to the much lower-dimensional coefficients of the parameter projection. The method is demonstrated to be more accurate and efficient than SVGD. It is also shown to be more scalable with respect to the number of parameters, samples, data points, and processor cores via experiments with parameters dimensions ranging from the hundreds to the tens of thousands.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Feb 2020.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 7; 2024: 10; 2025: 3; 2026: 3.

33 papers.

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  1. 2022Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learningcited by 44
  2. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  3. 2021Projected Wasserstein gradient descent for high-dimensional Bayesian inferencecited by 24
  4. 2021On Stein Variational Neural Network Ensemblescited by 21
  5. 2021Neural Variational Gradient Descentcited by 19
  6. 2024Derivative-enhanced Deep Operator Networkcited by 19
  7. 2023Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariancecited by 16
  8. 2023Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descentcited by 13
  9. 2022Grassmann Stein Variational Gradient Descentcited by 12
  10. 2022Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimizationcited by 9
  11. 2022Feature Space Particle Inference for Neural Network Ensemblescited by 9
  12. 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Ratescited by 8
21 more
  1. 2024Path-Guided Particle-based Samplingcited by 8
  2. 2024Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversioncited by 5
  3. 2025Are We Really Learning the Score Function? Reinterpreting Diffusion Models Through Wasserstein Gradient Flow Matchingcited by 5
  4. 2021Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inferencecited by 4
  5. 2021Stein Variational Gradient Descent with Multiple Kernelcited by 4
  6. 2022A stochastic Stein Variational Newton methodcited by 4
  7. 2024Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network modelscited by 4
  8. 2023Learning Rate Free Sampling in Constrained Domainscited by 3
  9. 2024Bayesian Multi-Task Transfer Learning for Soft Prompt Tuningcited by 3
  10. 2024Dimension reduction via score ratio matchingcited by 2
  11. 2023Score Operator Newton transportcited by 1
  12. 2024Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processescited by 1
  13. 2025ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPscited by 1
  14. 2026Radon--Wasserstein Gradient Flows for Interacting-Particle Sampling in High Dimensionscited by 1
  15. 2023Augmented Message Passing Stein Variational Gradient Descentcited by 0
  16. 2024Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networkscited by 0
  17. 2024A Trust-Region Method for Graphical Stein Variational Inferencecited by 0
  18. 2024Stein Variational Newton Neural Network Ensemblescited by 0
  19. 2025Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identificationcited by 0
  20. 2026Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantificationcited by 0
  21. 2026Wasserstein Gradient Flows and Forward-Only Diffusion Are Not Enough for Multimodal Samplingcited by 0