mlsanityData to 14 Oct 2026

Variational Boosting: Iteratively Refining Posterior Approximations

arXiv 1611.06585 ↗First posted 20 Nov 2016stat.ML cs.LG stat.MEcited by 37

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Abstract (arXiv)

We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trade computation time for accuracy. We show how to expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that resulting posterior inferences compare favorably to existing posterior approximation algorithms in both accuracy and efficiency.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Nov 2016.

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Citing papers per year: 2020: 1; 2021: 10; 2022: 6; 2023: 5; 2024: 5; 2025: 7; 2026: 3.

37 papers.

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  1. 2021Challenges and Opportunities in High-dimensional Variational Inferencecited by 35
  2. 2021Is MC Dropout Bayesian?cited by 35
  3. 2021Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergencecited by 29
  4. 2022A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Modelscited by 22
  5. 2023Riemannian Laplace approximations for Bayesian neural networkscited by 15
  6. 2022Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networkscited by 14
  7. 2024A theoretical perspective on mode collapse in variational inferencecited by 14
  8. 2022MixFlows: principled variational inference via mixed flowscited by 11
  9. 2024EigenVI: score-based variational inference with orthogonal function expansionscited by 11
  10. 2021Statistical Perspectives on Reliability of Artificial Intelligence Systemscited by 10
  11. 2021The computational asymptotics of Gaussian variational inference and the Laplace approximationcited by 8
  12. 2024Batch, match, and patch: low-rank approximations for score-based variational inferencecited by 8
25 more
  1. 2022Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selectioncited by 7
  2. 2021Variational Marginal Particle Filterscited by 6
  3. 2020Learning from demonstration using products of experts: applications to manipulation and task prioritizationcited by 4
  4. 2025End-To-End Learning of Gaussian Mixture Priors for Diffusion Samplercited by 4
  5. 2025Boosted GFlowNets: Improving Exploration via Sequential Learningcited by 4
  6. 2021Interpolating between sampling and variational inference with infinite stochastic mixturescited by 3
  7. 2021Variational Inference with Holder Boundscited by 3
  8. 2023Fast post-process Bayesian inference with Variational Sparse Bayesian Quadraturecited by 3
  9. 2024Tree-based variational inference for Poisson log-normal modelscited by 2
  10. 2025Fisher meets Feynman: score-based variational inference with a product of expertscited by 2
  11. 2022Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluationcited by 1
  12. 2022VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognitioncited by 1
  13. 2023Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returnscited by 1
  14. 2024ELBOing Stein: Variational Bayes with Stein Mixture Inferencecited by 1
  15. 2025Quantification of Uncertainties in Probabilistic Deep Neural Network by Implementing Boosting of Variational Inferencecited by 1
  16. 2025Asymptotically exact variational flows via involutive MCMC kernelscited by 1
  17. 2025Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networkscited by 1
  18. 2021Boltzmann Tuning of Generative Modelscited by 0
  19. 2021Boosting Variational Inference With Locally Adaptive Step-Sizescited by 0
  20. 2023Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Modelscited by 0
  21. 2023Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximationscited by 0
  22. 2025Measuring the right thing: justifying metrics in AI impact assessmentscited by 0
  23. 2026Stepwise Variational Inference with Vine Copulascited by 0
  24. 2026Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principlecited by 0
  25. 2026Adaptive mixture variational inference for spike-and-slab regressioncited by 0