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MCMC-driven learning

arXiv 2402.09598 ↗First posted 14 Feb 2024stat.ML cs.LG math.STcited by 0

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Abstract (arXiv)

This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chain Monte Carlo (MCMC) and machine learningunicodex2014which includes black-box variational inference, adaptive MCMC, normalizing flow construction and transport-assisted MCMC, surrogate-likelihood MCMC, coreset construction for MCMC with big data, Markov chain gradient descent, Markovian score climbing, and moreunicodex2014within one common framework. By doing so, the theory and methods developed for each may be translated and generalized.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 132 references that are in the map. First posted on arXiv 14 Feb 2024.

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29 of 132 references.

References · 29

  1. 2016Density estimation using Real NVPcited by 2,029
  2. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  3. 2019On the Convergence of Adam and Beyondcited by 834
  4. 2013Black Box Variational Inferencecited by 317
  5. 2019Monte Carlo Gradient Estimation in Machine Learningcited by 281
  6. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 217
  7. 2023DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedulecited by 85
  8. 2016Coresets for Scalable Bayesian Logistic Regressioncited by 79
  9. 2019NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transportcited by 73
  10. 2022Continual Repeated Annealed Flow Transport Monte Carlocited by 66
  11. 2018On Markov Chain Gradient Descentcited by 63
  12. 2022Making SGD Parameter-Freecited by 50
17 more
  1. 2022Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximationcited by 41
  2. 2021MCMC Variational Inference via Uncorrected Hamiltonian Annealingcited by 39
  3. 2021Differentiable Annealed Importance Sampling and the Perils of Gradient Noisecited by 36
  4. 2019Sparse Variational Inference: Bayesian Coresets from Scratchcited by 28
  5. 2021Local-Global MCMC kernels: the best of both worldscited by 21
  6. 2023On Sampling with Approximate Transport Mapscited by 20
  7. 2016An introduction to sampling via measure transportcited by 16
  8. 2021Surrogate Likelihoods for Variational Annealed Importance Samplingcited by 14
  9. 2022Bayesian inference via sparse Hamiltonian flowscited by 12
  10. 2022Transport Elliptical Slice Samplingcited by 12
  11. 2022Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinementcited by 9
  12. 2022Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradientscited by 6
  13. 2018Air Markov Chain Monte Carlocited by 5
  14. 2023Coreset Markov Chain Monte Carlocited by 5
  15. 2024A review of Monte Carlo-based versions of the EM algorithmcited by 4
  16. 2020Stochastic Normalizing Flowscited by 2
  17. 2023Pigeons.jl: Distributed Sampling From Intractable Distributionscited by 1

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