mlsanityData to 7 Oct 2026

A Stein variational Newton method

arXiv 1806.03085 ↗First posted 8 Jun 2018stat.ML cs.LG cs.NAcited by 49

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Abstract (arXiv)

Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reproducing kernel Hilbert space. In this paper, we accelerate and generalize the SVGD algorithm by including second-order information, thereby approximating a Newton-like iteration in function space. We also show how second-order information can lead to more effective choices of kernel. We observe significant computational gains over the original SVGD algorithm in multiple test cases.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2018.

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Citing papers per year: 2021: 7; 2022: 11; 2023: 13; 2024: 9; 2025: 7; 2026: 2.

49 papers.

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  1. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
  2. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  3. 2022Semi-supervised Invertible Neural Operators for Bayesian Inverse Problemscited by 25
  4. 2021Projected Wasserstein gradient descent for high-dimensional Bayesian inferencecited by 24
  5. 2023SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identitycited by 23
  6. 2021On Stein Variational Neural Network Ensemblescited by 21
  7. 2024Outlier-robust Kalman Filtering through Generalised Bayescited by 21
  8. 2022Particle-based Variational Inference with Preconditioned Functional Gradient Flowcited by 20
  9. 2023Sampling via Gradient Flows in the Space of Probability Measurescited by 20
  10. 2021Neural Variational Gradient Descentcited by 19
  11. 2021Sampling with Mirrored Stein Operatorscited by 17
  12. 2022Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Conditioncited by 16
37 more
  1. 2023Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariancecited by 16
  2. 2023Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descentcited by 13
  3. 2022Grassmann Stein Variational Gradient Descentcited by 12
  4. 2023Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Bettingcited by 11
  5. 2023Adaptive operator learning for infinite-dimensional Bayesian inverse problemscited by 11
  6. 2024Stein transport for Bayesian inferencecited by 11
  7. 2024Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributionscited by 11
  8. 2023Particle-based Variational Inference with Generalized Wasserstein Gradient Flowcited by 10
  9. 2022Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimizationcited by 9
  10. 2022Batch Bayesian optimisation via density-ratio estimation with guaranteescited by 9
  11. 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Ratescited by 8
  12. 2024Kernel Semi-Implicit Variational Inferencecited by 8
  13. 2021Performance analysis of greedy algorithms for minimising a Maximum Mean Discrepancycited by 6
  14. 2021Multilevel Stein variational gradient descent with applications to Bayesian inverse problemscited by 4
  15. 2022A stochastic Stein Variational Newton methodcited by 4
  16. 2022A Note on the Convergence of Mirrored Stein Variational Gradient Descent under (L0,L1)-Smoothness Conditioncited by 4
  17. 2024Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learningcited by 4
  18. 2021Stein Variational Gradient Descent with Multiple Kernelcited by 3
  19. 2022Online, Informative MCMC Thinning with Kernelized Stein Discrepancycited by 3
  20. 2023Learning Rate Free Sampling in Constrained Domainscited by 3
  21. 2025New affine invariant ensemble samplers and their dimensional scalingcited by 3
  22. 2022Improved Stein Variational Gradient Descent with Importance Weightscited by 2
  23. 2024Stein Variational Evolution Strategiescited by 2
  24. 2023Score Operator Newton transportcited by 1
  25. 2024ELBOing Stein: Variational Bayes with Stein Mixture Inferencecited by 1
  26. 2025Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Pointscited by 1
  27. 2026A Kernel Approach for Semi-implicit Variational Inferencecited by 1
  28. 2022Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice modelscited by 0
  29. 2023Augmented Message Passing Stein Variational Gradient Descentcited by 0
  30. 2024A Trust-Region Method for Graphical Stein Variational Inferencecited by 0
  31. 2024Stein Variational Newton Neural Network Ensemblescited by 0
  32. 2025Accelerating Particle-based Energetic Variational Inferencecited by 0
  33. 2025A Taxonomy of Self-Handovercited by 0
  34. 2025Revisiting Unbiased Implicit Variational Inferencecited by 0
  35. 2025Semi-Implicit Variational Inference via Kernelized Path Gradient Descentcited by 0
  36. 2025Adaptive Kernel Selection for Stein Variational Gradient Descentcited by 0
  37. 2026Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentialscited by 0