mlsanityData to 7 Oct 2026

Provable Gradient Variance Guarantees for Black-Box Variational Inference

arXiv 1906.08241 ↗First posted 19 Jun 2019cs.LG stat.MLcited by 21

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Abstract (arXiv)

Recent variational inference methods use stochastic gradient estimators whose variance is not well understood. Theoretical guarantees for these estimators are important to understand when these methods will or will not work. This paper gives bounds for the common "reparameterization" estimators when the target is smooth and the variational family is a location-scale distribution. These bounds are unimprovable and thus provide the best possible guarantees under the stated assumptions.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2019.

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

21 papers.

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  1. 2023Provable convergence guarantees for black-box variational inferencecited by 29
  2. 2023On the Convergence of Black-Box Variational Inferencecited by 22
  3. 2024Batch and match: black-box variational inference with a score-based divergencecited by 22
  4. 2023Variational Inference with Gaussian Score Matchingcited by 17
  5. 2023Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?cited by 10
  6. 2024Kernel Semi-Implicit Variational Inferencecited by 8
  7. 2021Mixture weights optimisation for Alpha-Divergence Variational Inferencecited by 6
  8. 2023Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inferencecited by 6
  9. 2024On Divergence Measures for Training GFlowNetscited by 6
  10. 2024Provably Scalable Black-Box Variational Inference with Structured Variational Familiescited by 4
  11. 2025Optimization Guarantees for Square-Root Natural-Gradient Variational Inferencecited by 3
  12. 2022DPVIm: Differentially Private Variational Inference Improvedcited by 2
9 more
  1. 2024Demystifying SGD with Doubly Stochastic Gradientscited by 2
  2. 2024Understanding and mitigating difficulties in posterior predictive evaluationcited by 1
  3. 2025Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inferencecited by 1
  4. 2026A Kernel Approach for Semi-implicit Variational Inferencecited by 1
  5. 2026Large-scale Score-based Variational Posterior Inference for Bayesian Deep Neural Networkscited by 1
  6. 2026Convergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedulescited by 1
  7. 2026Does "Do Differentiable Simulators Give Better Policy Gradients?'' Give Better Policy Gradients?cited by 0
  8. 2026SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batchingcited by 0
  9. 2026Large-scale empirical tuning and comparison of default optimizers for variational inferencecited by 0