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Importance Weighting and Variational Inference

arXiv 1808.09034 ↗First posted 27 Aug 2018cs.LG stat.MLcited by 55

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Abstract (arXiv)

Recent work used importance sampling ideas for better variational bounds on likelihoods. We clarify the applicability of these ideas to pure probabilistic inference, by showing the resulting Importance Weighted Variational Inference (IWVI) technique is an instance of augmented variational inference, thus identifying the looseness in previous work. Experiments confirm IWVI's practicality for probabilistic inference. As a second contribution, we investigate inference with elliptical distributions, which improves accuracy in low dimensions, and convergence in high dimensions.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2018.

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Citing papers per year: 2021: 14; 2022: 13; 2023: 8; 2024: 9; 2025: 2; 2026: 9.

55 papers.

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  1. 2022GFlowNets and variational inferencecited by 103
  2. 2021Annealed Flow Transport Monte Carlocited by 90
  3. 2024Probabilistic Inference in Language Models via Twisted Sequential Monte Carlocited by 85
  4. 2022Variational methods for simulation-based inferencecited by 43
  5. 2021MCMC Variational Inference via Uncorrected Hamiltonian Annealingcited by 39
  6. 2021Monte Carlo Variational Auto-Encoderscited by 36
  7. 2021Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergencecited by 29
  8. 2021Improving Lossless Compression Rates via Monte Carlo Bits-Back Codingcited by 19
  9. 2021Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inferencecited by 18
  10. 2023Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Boxcited by 17
  11. 2022Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptoticscited by 15
  12. 2021Surrogate Likelihoods for Variational Annealed Importance Samplingcited by 14
38 more
  1. 2022Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximationscited by 12
  2. 2021Empirical Evaluation of Biased Methods for Alpha Divergence Minimizationcited by 10
  3. 2024A Generative Model of Symmetry Transformationscited by 10
  4. 2024Kernel Semi-Implicit Variational Inferencecited by 8
  5. 2022Conditional set generation using Seq2seq modelscited by 7
  6. 2023Adaptive Annealed Importance Sampling with Constant Rate Progresscited by 7
  7. 2024Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inferencecited by 7
  8. 2021Variational Marginal Particle Filterscited by 6
  9. 2022Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradientscited by 6
  10. 2023Sample Average Approximation for Black-Box VIcited by 6
  11. 2021Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalizationcited by 5
  12. 2021Lifelong Generative Modelling Using Dynamic Expansion Graph Modelcited by 4
  13. 2022Variational Inference with Locally Enhanced Bounds for Hierarchical Modelscited by 4
  14. 2022Black-box Coreset Variational Inferencecited by 4
  15. 2023Forward chi2 Divergence Based Variational Importance Samplingcited by 4
  16. 2021An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulationscited by 3
  17. 2022Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectivescited by 3
  18. 2022Compound Density Networks for Risk Prediction using Electronic Health Recordscited by 3
  19. 2023U-Statistics for Importance-Weighted Variational Inferencecited by 3
  20. 2023Adaptive importance sampling for heavy-tailed distributions via α-divergence minimizationcited by 3
  21. 2024Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Samplingcited by 3
  22. 2022Reconstruction of Incomplete Wildfire Data using Deep Generative Modelscited by 2
  23. 2024Differentiable Annealed Importance Sampling Minimizes The Symmetrized Kullback-Leibler Divergence Between Initial and Target Distributioncited by 2
  24. 2021Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Seriescited by 1
  25. 2022Optimization of Annealed Importance Sampling Hyperparameterscited by 1
  26. 2022Explainability as statistical inferencecited by 1
  27. 2023A Targeted Accuracy Diagnostic for Variational Approximationscited by 1
  28. 2023Bootstrap Your Own Variancecited by 1
  29. 2024Understanding and mitigating difficulties in posterior predictive evaluationcited by 1
  30. 2025Globally Convergent Variational Inferencecited by 1
  31. 2025Censor Dependent Variational Inferencecited by 1
  32. 2026A Kernel Approach for Semi-implicit Variational Inferencecited by 1
  33. 2026How to Approximate Inference with Subtractive Mixture Modelscited by 1
  34. 2021Variational Rejection Particle Filteringcited by 0
  35. 2024Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoderscited by 0
  36. 2024Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networkscited by 0
  37. 2026Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applicationscited by 0
  38. 2026Variational Learning of Fractional Posteriorscited by 0

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