mlsanityData to 7 Oct 2026

Yes, but Did It Work?: Evaluating Variational Inference

arXiv 1802.02538 ↗First posted 7 Feb 2018stat.ML stat.COcited by 52

Not on the map. Why?

Abstract (arXiv)

While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation. We propose two diagnostic algorithms to alleviate this problem. The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate. The variational simulation-based calibration (VSBC) assesses the average performance of point estimates.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Feb 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 6; 2023: 12; 2024: 9; 2025: 7; 2026: 9.

52 papers.

Sort:

  1. 2021Challenges and Opportunities in High-dimensional Variational Inferencecited by 35
  2. 2021Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergencecited by 29
  3. 2023Provable convergence guarantees for black-box variational inferencecited by 29
  4. 2021Pathfinder: Parallel quasi-Newton variational inferencecited by 26
  5. 2023On the Convergence of Black-Box Variational Inferencecited by 22
  6. 2021q-Paths: Generalizing the Geometric Annealing Path using Power Meanscited by 12
  7. 2022ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Imagescited by 12
  8. 2024Amortized Bayesian Multilevel Modelscited by 12
  9. 2025STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classificationcited by 12
  10. 2021PriorVAE: Encoding spatial priors with VAEs for small-area estimationcited by 11
  11. 2024Variational Inference for Uncertainty Quantification: an Analysis of Trade-offscited by 11
  12. 2024Flexible Tails for Normalizing Flowscited by 11
38 more
  1. 2024Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrixcited by 9
  2. 2021Toward a Taxonomy of Trust for Probabilistic Machine Learningcited by 8
  3. 2022Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transportcited by 8
  4. 2023Variational Latent Branching Model for Off-Policy Evaluationcited by 6
  5. 2023Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inferencecited by 6
  6. 2023Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasmacited by 6
  7. 2021Monotonic Alpha-divergence Minimisation for Variational Inferencecited by 5
  8. 2022A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of termscited by 5
  9. 2023Variational Inference with Coverage Guarantees in Simulation-Based Inferencecited by 5
  10. 2023Forward chi2 Divergence Based Variational Importance Samplingcited by 4
  11. 2021An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulationscited by 3
  12. 2022Bernstein Flows for Flexible Posteriors in Variational Bayescited by 3
  13. 2023Fast post-process Bayesian inference with Variational Sparse Bayesian Quadraturecited by 3
  14. 2024Amortized Bayesian Workflowcited by 3
  15. 2025Posterior SBC: Simulation-Based Calibration Checking Conditional on Datacited by 3
  16. 2025Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetrycited by 3
  17. 2022Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inferencecited by 2
  18. 2022Scalable Semi-Modular Inference with Variational Meta-Posteriorscited by 2
  19. 2023Discriminative calibration: Check Bayesian computation from simulations and flexible classifiercited by 2
  20. 2023A Heavy-Tailed Algebra for Probabilistic Programmingcited by 2
  21. 2023Variational Bayesian Neural Networks via Resolution of Singularitiescited by 1
  22. 2023A Targeted Accuracy Diagnostic for Variational Approximationscited by 1
  23. 2024SoftCVI: Contrastive variational inference with self-generated soft labelscited by 1
  24. 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Modelscited by 1
  25. 2025An Image is Worth K Topics: A Visual Structural Topic Model with Pretrained Image Embeddingscited by 1
  26. 2025Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluationscited by 1
  27. 2025Semi-supervised Clustering Through Representation Learning of Large-scale EHR Datacited by 1
  28. 2025Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Imagescited by 1
  29. 2026How to Approximate Inference with Subtractive Mixture Modelscited by 1
  30. 2026Even More Guarantees for Variational Inference in the Presence of Symmetriescited by 1
  31. 2021Validating Gaussian Process Models with Simulation-Based Calibrationcited by 0
  32. 2024Pathwise Gradient Variance Reduction with Control Variates in Variational Inferencecited by 0
  33. 2024Variational Bayesian Bow tie Neural Networks with Shrinkagecited by 0
  34. 2026MDP Planning as Policy Inferencecited by 0
  35. 2026Variational Learning of Fractional Posteriorscited by 0
  36. 2026Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusterscited by 0
  37. 2026Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Modelscited by 0
  38. 2026Large-scale empirical tuning and comparison of default optimizers for variational inferencecited by 0

50 most cited of 52 All 52 (JSON)