Paper Citation Record · LEDGER

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.02910.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.02910 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:39:11.477985Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact2
verified fuzzy20
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eaaa870d-8252-48e0-bd8b-297a01a43a50 · outbound

This paper cites Distributed delayed stochastic optimization.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Distributed delayed stochastic optimization

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.434871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e30ade1e-c653-4bcd-abab-22e55387820a · outbound

This paper cites Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.161950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c92c048-ea6f-474f-818b-ca96f2d665b3 · outbound

This paper cites The pseudo-marginal approach for eﬃcient monte carlo computations.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal The pseudo-marginal approach for eﬃcient monte carlo computations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.414830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7df05cd4-e9e3-4762-8d30-fca6cfbfa553 · outbound

This paper cites Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.391943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d407ac4c-e6a4-4a67-93bf-2ce33cb6835c · outbound

This paper cites On Markov chain Monte Carlo methods for tall data.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal On Markov chain Monte Carlo methods for tall data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.187935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b772f92-ae72-4b2e-b174-78dd69f2960e · outbound

This paper cites Spectrally-normalized margin bounds for neural networks.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Spectrally-normalized margin bounds for neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.370321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d27adf2-50e7-4623-be5a-2f1c1dfdd661 · outbound

This paper cites The zig-zag process and super- eﬃcient sampling for bayesian analysis of big data.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal The zig-zag process and super- eﬃcient sampling for bayesian analysis of big data

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.345026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a1ffb179-596c-48a0-b53b-0fb5068cc2ec · outbound

This paper cites Variational inference: A review for statisticians.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Variational inference: A review for statisticians

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.321052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d6d5fdda-8e9a-4b67-83c9-e85f5cd006f9 · outbound

This paper cites An Efficient Minibatch Acceptance Test for Metropolis-Hastings.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal An Efficient Minibatch Acceptance Test for Metropolis-Hastings

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.222423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c2f5259-8c03-46fc-8fce-16853f6d7bf7 · outbound

This paper cites Stochastic gradient hamiltonian monte carlo.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Stochastic gradient hamiltonian monte carlo

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.229657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d5929b0-5f7d-4d8a-b09c-c2827f2d2877 · outbound

This paper cites Minibatch Gibbs Sampling on Large Graphical Models.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Minibatch Gibbs Sampling on Large Graphical Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:39:11.782722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0be4dac3-4216-481e-8b6c-033f7f05d2ad · outbound

This paper cites An Instability in Variational Inference for Topic Models.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal An Instability in Variational Inference for Topic Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:39:11.746968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 58aa6ced-17bf-4964-a4c9-e645cb24df24 · outbound

This paper cites On nonnegative unbiased estimators.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal On nonnegative unbiased estimators

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.279288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dd65e0e6-3172-4ca4-adb2-3ad196121253 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Adam: A Method for Stochastic Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.267031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12538733-fb76-4fb8-8b38-e2b21dcc1982 · outbound

This paper cites Austerity in MCMC land: Cutting the Metropolis-Hastings budget.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Austerity in MCMC land: Cutting the Metropolis-Hastings budget

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.248216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 995d52e3-a3d5-445f-bdfe-ba9c74f83d1a · outbound

This paper cites Learning multiple layers of features from tiny images.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Learning multiple layers of features from tiny images

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.218789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 264a47ad-37f5-4ac1-952d-edf48ba38687 · outbound

This paper cites Preconditioned stochastic gradient langevin dynamics for deep neural networks.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Preconditioned stochastic gradient langevin dynamics for deep neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.198596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64c7664c-bbfa-452c-88e1-7b5e31badc29 · outbound

This paper cites Mini-batch Tempered MCMC.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Mini-batch Tempered MCMC

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.311897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c16b2033-f958-440e-b95a-6d336915c3aa · outbound

This paper cites Fireﬂy Monte Carlo: Exact MCMC with subsets of data.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Fireﬂy Monte Carlo: Exact MCMC with subsets of data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.171000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fdc84b1c-3a22-40ef-9589-241f5d84c44a · outbound

This paper cites Mean ﬁeld for the stochastic blockmodel: Optimization landscape and convergence issues.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Mean ﬁeld for the stochastic blockmodel: Optimization landscape and convergence issues

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.144986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 467ddf46-6637-4aca-bc7d-671a7c9ac397 · outbound

This paper cites Mcmc using hamiltonian dynamics.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Mcmc using hamiltonian dynamics

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.344358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d633f801-0768-4f9b-ba7b-13441f2843f2 · outbound

This paper cites Asymptotically exact, embarrassingly parallel mcmc.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Asymptotically exact, embarrassingly parallel mcmc

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.091351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7b8881a4-7daa-4253-8a89-09395d8f9a13 · outbound

This paper cites Speeding up mcmc by eﬃcient data subsampling.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Speeding up mcmc by eﬃcient data subsampling

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.058296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4aa40953-b6a3-4348-929a-7da0a42eecd2 · outbound

This paper cites Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.380209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cceea58-7356-4eeb-a413-555004313d97 · outbound

This paper cites A stochastic approximation method.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal A stochastic approximation method

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:12.028671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 05f90bec-9973-4d62-b8b9-c4353f060f23 · outbound

This paper cites Exponential convergence of langevin distributions and their discrete approximations.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Exponential convergence of langevin distributions and their discrete approximations

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:11.996858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1a5a6e9f-ffc0-4144-9853-6006c360298f · outbound

This paper cites Bayes and big data: The consensus monte carlo algorithm.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Bayes and big data: The consensus monte carlo algorithm

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:11.974188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 775bbaf8-8184-45d5-85df-32e3f0992b93 · outbound

This paper cites Consistency and ﬂuctua- tions for stochastic gradient langevin dynamics.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Consistency and ﬂuctua- tions for stochastic gradient langevin dynamics

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:11.952722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ba302be3-3804-4600-94b4-08b0aff92795 · outbound

This paper cites Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:11.932836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c521ec57-6e9e-478a-8543-bba25aa74ff6 · outbound

This paper cites Parallelizing MCMC via Weierstrass Sampler.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Parallelizing MCMC via Weierstrass Sampler

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.437982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56d1231c-6a06-44ea-9b1e-0831384770a9 · outbound

This paper cites Bayesian learning via stochastic gradient Langevin dynamics.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Bayesian learning via stochastic gradient Langevin dynamics

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:39:11.910572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2255279d-81a7-4dc8-b70c-46f503aad0b8 · outbound

This paper cites A Walk with SGD.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal A Walk with SGD

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.468881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3d05fdc-0f21-4111-ae06-0a331a3607a9 · outbound

This paper cites Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks.

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-14T14:39:11.477985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.