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Variational Rejection Particle Filtering

arXiv 2103.15343 ↗First posted 29 Mar 2021cs.LGcited by 0

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Abstract (arXiv)

We present a variational inference (VI) framework that unifies and leverages sequential Monte-Carlo (particle filtering) with approximate rejection sampling to construct a flexible family of variational distributions. Furthermore, we augment this approach with a resampling step via Bernoulli race, a generalization of a Bernoulli factory, to obtain a low-variance estimator of the marginal likelihood. Our framework, Variational Rejection Particle Filtering (VRPF), leads to novel variational bounds on the marginal likelihood, which can be optimized efficiently with respect to the variational parameters and generalizes several existing approaches in the VI literature. We also present theoretical properties of the variational bound and demonstrate experiments on various models of sequential data, such as the Gaussian state-space model and variational recurrent neural net (VRNN), on which VRPF outperforms various existing state-of-the-art VI methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 44 references that are in the map. First posted on arXiv 29 Mar 2021.

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25 of 44 references.

References · 25

  1. 2015Variational Inference with Normalizing Flowscited by 2,161
  2. 2016Variational Inference: A Review for Statisticianscited by 1,466
  3. 2015Importance Weighted Autoencoderscited by 454
  4. 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
  5. 2013Black Box Variational Inferencecited by 317
  6. 2017Variational Sequential Monte Carlocited by 93
  7. 2017Filtering Variational Objectivescited by 91
  8. 2015Hierarchical Variational Modelscited by 89
  9. 2017Auto-Encoding Sequential Monte Carlocited by 64
  10. 2018Resampled Priors for Variational Autoencoderscited by 56
  11. 2018Importance Weighting and Variational Inferencecited by 55
  12. 2018Discriminator Rejection Samplingcited by 48
13 more
  1. 2018Metropolis-Hastings Generative Adversarial Networkscited by 40
  2. 2020Regularized Autoencoders via Relaxed Injective Probability Flowcited by 23
  3. 2016Reparameterization Gradients through Acceptance-Rejection Sampling Algorithmscited by 22
  4. 2017Approximate Inference with Amortised MCMCcited by 22
  5. 2019A Contrastive Divergence for Combining Variational Inference and MCMCcited by 20
  6. 2019Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximationcited by 14
  7. 2018Variational Rejection Samplingcited by 13
  8. 2017Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributionscited by 7
  9. 2018Metropolis-Hastings view on variational inference and adversarial trainingcited by 5
  10. 2017A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVIcited by 4
  11. 2017Bernoulli Factories and Black-Box Reductions in Mechanism Designcited by 3
  12. 2019Bernoulli Race Particle Filterscited by 1
  13. 2019Particle filter with rejection control and unbiased estimator of the marginal likelihoodcited by 1

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