mlsanityData to 14 Oct 2026

Stein Points

arXiv 1803.10161 ↗First posted 27 Mar 2018stat.CO cs.LG stat.MLcited by 43

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Abstract (arXiv)

An important task in computational statistics and machine learning is to approximate a posterior distribution p(x) with an empirical measure supported on a set of representative points xii=1n. This paper focuses on methods where the selection of points is essentially deterministic, with an emphasis on achieving accurate approximation when n is small. To this end, we present `Stein Points'. The idea is to exploit either a greedy or a conditional gradient method to iteratively minimise a kernel Stein discrepancy between the empirical measure and p(x). Our empirical results demonstrate that Stein Points enable accurate approximation of the posterior at modest computational cost. In addition, theoretical results are provided to establish convergence of the method.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Mar 2018.

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Citing papers per year: 2021: 10; 2022: 4; 2023: 7; 2024: 5; 2025: 10; 2026: 7.

43 papers.

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  1. 2021Kernel Stein Discrepancy Descentcited by 57
  2. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
  3. 2021Kernel Thinningcited by 47
  4. 2021Generalized Kernel Thinningcited by 29
  5. 2022A Finite-Particle Convergence Rate for Stein Variational Gradient Descentcited by 22
  6. 2021Sampling with Mirrored Stein Operatorscited by 17
  7. 2024Generalized Coverage for More Robust Low-Budget Active Learningcited by 16
  8. 2022Targeted Separation and Convergence with Kernel Discrepanciescited by 15
  9. 2022Controlling Moments with Kernel Stein Discrepanciescited by 13
  10. 2023Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inferencecited by 13
  11. 2025Weighted quantization using MMD: From mean field to mean shift via gradient flowscited by 12
  12. 2023Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularizationcited by 9
31 more
  1. 2023GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Frameworkcited by 9
  2. 2025A Dictionary of Closed-Form Kernel Mean Embeddingscited by 9
  3. 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Ratescited by 8
  4. 2021Performance analysis of greedy algorithms for minimising a Maximum Mean Discrepancycited by 7
  5. 2021DPVI: A Dynamic-Weight Particle-Based Variational Inference Frameworkcited by 7
  6. 2023Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approachcited by 6
  7. 2026Probabilistic Inference and Learning with Stein's Methodcited by 5
  8. 2021Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fitcited by 4
  9. 2023Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problemscited by 4
  10. 2023When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimalitycited by 4
  11. 2024Nystr"om Kernel Stein Discrepancycited by 4
  12. 2024Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamicscited by 4
  13. 2021Compressed Monte Carlo with application in particle filteringcited by 3
  14. 2021A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernelcited by 3
  15. 2025The Minimax Lower Bound of Kernel Stein Discrepancy Estimationcited by 3
  16. 2026Minimax Estimation of Kernel Stein Discrepancy: Trace versus Hilbert-Schmidt Scalescited by 3
  17. 2025Low Stein Discrepancy via Message-Passing Monte Carlocited by 2
  18. 2026To discretize continually: Mean shift interacting particle systems for Bayesian inferencecited by 2
  19. 2026Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSDcited by 2
  20. 2024Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herdingcited by 1
  21. 2025Neural Low-Discrepancy Sequencescited by 1
  22. 2025Optimizing Kernel Discrepancies via Subset Selectioncited by 1
  23. 2021Centroid Approximation for Bootstrap: Improving Particle Quality at Inferencecited by 0
  24. 2024Deterministic Fokker-Planck Transport -- With Applications to Sampling, Variational Inference, Kernel Mean Embeddings & Sequential Monte Carlocited by 0
  25. 2025Stein Discrepancy for Unsupervised Domain Adaptationcited by 0
  26. 2025Quenched large deviations for Monte Carlo integration with Coulomb gasescited by 0
  27. 2025Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modelingcited by 0
  28. 2025BayesSum: Bayesian Quadrature in Discrete Spacescited by 0
  29. 2026StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flowcited by 0
  30. 2026Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentialscited by 0
  31. 2026Amortized quadrature for posterior expectations in inverse problemscited by 0