mlsanityData to 7 Oct 2026

Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions

arXiv 1901.08659 ↗First posted 24 Jan 2019math.OCcited by 21

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Abstract (arXiv)

We propose a fast and scalable variational method for Bayesian inference in high-dimensional parameter space, which we call projected Stein variational Newton (pSVN) method. We exploit the intrinsic low-dimensional geometric structure of the posterior distribution in the high-dimensional parameter space via its Hessian (of the log posterior) operator and perform a parallel update of the parameter samples projected into a low-dimensional subspace by an SVN method. The subspace is adaptively constructed using the eigenvectors of the averaged Hessian at the current samples. We demonstrate fast convergence of the proposed method and its scalability with respect to the number of parameters, samples, and processor cores.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2019.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 1; 2024: 6; 2025: 2; 2026: 1.

21 papers.

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  1. 2022Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learningcited by 43
  2. 2024Sampling in Unit Time with Kernel Fisher-Rao Flowcited by 27
  3. 2021Projected Wasserstein gradient descent for high-dimensional Bayesian inferencecited by 24
  4. 2022Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problemscited by 21
  5. 2022Particle-based Variational Inference with Preconditioned Functional Gradient Flowcited by 20
  6. 2021Sampling with Mirrored Stein Operatorscited by 17
  7. 2023Adaptive operator learning for infinite-dimensional Bayesian inverse problemscited by 11
  8. 2021Multilevel Stein variational gradient descent with applications to Bayesian inverse problemscited by 4
  9. 2021Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inferencecited by 4
  10. 2022A stochastic Stein Variational Newton methodcited by 4
  11. 2024Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network modelscited by 4
  12. 2021Active Slices for Sliced Stein Discrepancycited by 3
9 more
  1. 2021A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernelcited by 3
  2. 2024Stein Variational Evolution Strategiescited by 2
  3. 2026Dual-space posterior sampling for Bayesian inference in constrained inverse problemscited by 2
  4. 2024A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Featurescited by 1
  5. 2025Soft Quality-Diversity Optimizationcited by 1
  6. 2022Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice modelscited by 0
  7. 2024A Trust-Region Method for Graphical Stein Variational Inferencecited by 0
  8. 2024Stein Variational Newton Neural Network Ensemblescited by 0
  9. 2025Accelerating Particle-based Energetic Variational Inferencecited by 0