mlsanityData to 7 Oct 2026

Variational Sequential Monte Carlo

arXiv 1705.11140 ↗First posted 31 May 2017stat.ML stat.CO stat.MEcited by 93

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Abstract (arXiv)

Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 27; 2022: 11; 2023: 17; 2024: 16; 2025: 10; 2026: 12.

93 papers.

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  1. 2021KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamicscited by 128
  2. 2023Score-based Data Assimilationcited by 91
  3. 2021Annealed Flow Transport Monte Carlocited by 90
  4. 2022Continual Repeated Annealed Flow Transport Monte Carlocited by 66
  5. 2021Differentiable Particle Filtering via Entropy-Regularized Optimal Transportcited by 59
  6. 2022A Variational Perspective on Generative Flow Networkscited by 39
  7. 2021Differentiable Annealed Importance Sampling and the Perils of Gradient Noisecited by 36
  8. 2021Auto-differentiable Ensemble Kalman Filterscited by 28
  9. 2023An overview of differentiable particle filters for data-adaptive sequential Bayesian inferencecited by 23
  10. 2022SIXO: Smoothing Inference with Twisted Objectivescited by 22
  11. 2021Structured World Belief for Reinforcement Learning in POMDPcited by 21
  12. 2021Improving Lossless Compression Rates via Monte Carlo Bits-Back Codingcited by 19
38 more
  1. 2021Online Variational Filtering and Parameter Learningcited by 19
  2. 2021Differentiable Particle Filtering without Modifying the Forward Passcited by 18
  3. 2021Unsupervised Learned Kalman Filteringcited by 17
  4. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
  5. 2021Stein Variational Gradient Descent: many-particle and long-time asymptoticscited by 15
  6. 2024Inferring stochastic low-rank recurrent neural networks from neural datacited by 15
  7. 2021Nested Variational Inferencecited by 14
  8. 2021Surrogate Likelihoods for Variational Annealed Importance Samplingcited by 14
  9. 2022On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learningcited by 13
  10. 2021HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Samplingcited by 12
  11. 2023Reduced-Order Autodifferentiable Ensemble Kalman Filterscited by 12
  12. 2021On Incorporating Inductive Biases into VAEscited by 11
  13. 2021Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inferencecited by 10
  14. 2024Normalizing Flow-based Differentiable Particle Filterscited by 10
  15. 2024KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertaintycited by 9
  16. 2022Recursive Monte Carlo and Variational Inference with Auxiliary Variablescited by 8
  17. 2023Adaptive Annealed Importance Sampling with Constant Rate Progresscited by 7
  18. 2024Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inferencecited by 7
  19. 2024Meta-Dynamical State Space Models for Integrative Neural Data Analysiscited by 7
  20. 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
  21. 2021Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shootingcited by 6
  22. 2021Variational Marginal Particle Filterscited by 6
  23. 2021Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Modelscited by 6
  24. 2023Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systemscited by 5
  25. 2025Trust-Region Twisted Policy Improvementcited by 5
  26. 2023NAS-X: Neural Adaptive Smoothing via Twistingcited by 4
  27. 2023On Feynman--Kac training of partial Bayesian neural networkscited by 4
  28. 2023Online Variational Sequential Monte Carlocited by 4
  29. 2024Provably Scalable Black-Box Variational Inference with Structured Variational Familiescited by 4
  30. 2024Probabilistic Programming with Programmable Variational Inferencecited by 4
  31. 2025End-To-End Learning of Gaussian Mixture Priors for Diffusion Samplercited by 4
  32. 2021An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulationscited by 3
  33. 2021Self-Supervised Inference in State-Space Modelscited by 3
  34. 2022Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectivescited by 3
  35. 2022Particle-Based Score Estimation for State Space Model Learning in Autonomous Drivingcited by 3
  36. 2023U-Statistics for Importance-Weighted Variational Inferencecited by 3
  37. 2023Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariancecited by 3
  38. 2024Bayesian Multi-Task Transfer Learning for Soft Prompt Tuningcited by 3

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