NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport Article Swipe
Dustin Tran
,
Ian Langmore
,
Joshua S. Dillon
,
Matthew D. Hoffman
,
Pavel Sountsov
,
Vasudevan Srinivas
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1903.03704
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1903.03704
Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may take many expensive evaluations of the target distribution and its gradient to converge and mix. We propose neural transport (NeuTra) HMC, a technique for learning to correct this sort of unfavorable geometry using inverse autoregressive flows (IAF), a powerful neural variational inference technique. The IAF is trained to minimize the KL divergence from an isotropic Gaussian to the warped posterior, and then HMC sampling is performed in the warped space. We evaluate NeuTra HMC on a variety of synthetic and real problems, and find that it significantly outperforms vanilla HMC both in time to reach the stationary distribution and asymptotic effective-sample-size rates.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1903.03704
- https://arxiv.org/pdf/1903.03704
- OA Status
- green
- Cited By
- 65
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2920868047
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https://openalex.org/W2920868047Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1903.03704Digital Object Identifier
- Title
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NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural TransportWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2019Year of publication
- Publication date
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2019-03-09Full publication date if available
- Authors
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Dustin Tran, Ian Langmore, Joshua S. Dillon, Matthew D. Hoffman, Pavel Sountsov, Vasudevan SrinivasList of authors in order
- Landing page
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https://arxiv.org/abs/1903.03704Publisher landing page
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https://arxiv.org/pdf/1903.03704Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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Monte Carlo method, Gaussian, Autoregressive model, Hybrid Monte Carlo, Isotropy, Statistical physics, Mathematics, Posterior probability, Markov chain Monte Carlo, Applied mathematics, Algorithm, Computer science, Bayesian probability, Physics, Statistics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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65Total citation count in OpenAlex
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2025: 3, 2024: 4, 2023: 9, 2022: 6, 2021: 21Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.HMC | 86, 97, 113 |
| abstract_inverted_index.IAF | 68 |
| abstract_inverted_index.The | 67 |
| abstract_inverted_index.and | 32, 37, 84, 103, 106, 122 |
| abstract_inverted_index.for | 7, 47 |
| abstract_inverted_index.its | 33 |
| abstract_inverted_index.may | 23 |
| abstract_inverted_index.the | 15, 18, 29, 73, 81, 91, 119 |
| abstract_inverted_index.HMC, | 44 |
| abstract_inverted_index.both | 114 |
| abstract_inverted_index.find | 107 |
| abstract_inverted_index.from | 9, 76 |
| abstract_inverted_index.many | 25 |
| abstract_inverted_index.mix. | 38 |
| abstract_inverted_index.real | 104 |
| abstract_inverted_index.sort | 52 |
| abstract_inverted_index.take | 24 |
| abstract_inverted_index.that | 108 |
| abstract_inverted_index.then | 85 |
| abstract_inverted_index.this | 51 |
| abstract_inverted_index.time | 116 |
| abstract_inverted_index.when | 14 |
| abstract_inverted_index.Carlo | 2 |
| abstract_inverted_index.Monte | 1 |
| abstract_inverted_index.flows | 59 |
| abstract_inverted_index.reach | 118 |
| abstract_inverted_index.using | 56 |
| abstract_inverted_index.(IAF), | 60 |
| abstract_inverted_index.NeuTra | 96 |
| abstract_inverted_index.neural | 41, 63 |
| abstract_inverted_index.rates. | 125 |
| abstract_inverted_index.space. | 93 |
| abstract_inverted_index.target | 30 |
| abstract_inverted_index.warped | 82, 92 |
| abstract_inverted_index.correct | 50 |
| abstract_inverted_index.inverse | 57 |
| abstract_inverted_index.propose | 40 |
| abstract_inverted_index.trained | 70 |
| abstract_inverted_index.vanilla | 112 |
| abstract_inverted_index.variety | 100 |
| abstract_inverted_index.(NeuTra) | 43 |
| abstract_inverted_index.Gaussian | 79 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.converge | 36 |
| abstract_inverted_index.evaluate | 95 |
| abstract_inverted_index.geometry | 16, 55 |
| abstract_inverted_index.gradient | 34 |
| abstract_inverted_index.learning | 48 |
| abstract_inverted_index.minimize | 72 |
| abstract_inverted_index.powerful | 5, 62 |
| abstract_inverted_index.sampling | 8, 87 |
| abstract_inverted_index.algorithm | 6 |
| abstract_inverted_index.expensive | 26 |
| abstract_inverted_index.inference | 65 |
| abstract_inverted_index.isotropic | 78 |
| abstract_inverted_index.performed | 89 |
| abstract_inverted_index.posterior | 11, 19 |
| abstract_inverted_index.problems, | 105 |
| abstract_inverted_index.synthetic | 102 |
| abstract_inverted_index.technique | 46 |
| abstract_inverted_index.transport | 42 |
| abstract_inverted_index.asymptotic | 123 |
| abstract_inverted_index.divergence | 75 |
| abstract_inverted_index.posterior, | 83 |
| abstract_inverted_index.stationary | 120 |
| abstract_inverted_index.technique. | 66 |
| abstract_inverted_index.Hamiltonian | 0 |
| abstract_inverted_index.evaluations | 27 |
| abstract_inverted_index.outperforms | 111 |
| abstract_inverted_index.unfavorable | 54 |
| abstract_inverted_index.variational | 64 |
| abstract_inverted_index.distribution | 31, 121 |
| abstract_inverted_index.unfavorable, | 21 |
| abstract_inverted_index.significantly | 110 |
| abstract_inverted_index.autoregressive | 58 |
| abstract_inverted_index.distributions. | 12 |
| abstract_inverted_index.effective-sample-size | 124 |
| abstract_inverted_index.difficult-to-normalize | 10 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |