Paper Citation Record · LEDGER

Variational Inference for Latent Variable Models in High Dimensions

As of 7 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2506.01893.

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

pith.paper-citation-record.v1
2506.01893 v2

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 00e1ac81-29f9-47fa-97c6-2921c8a4b00e · outbound

This paper cites write newline.

Variational Inference for Latent Variable Models in High Dimensions write newline

Reference 1

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This paper cites ( 2018 ).

Variational Inference for Latent Variable Models in High Dimensions ( 2018 )

Reference 2

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Variational Inference for Latent Variable Models in High Dimensions Unresolved cited work

Reference 3

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Reference 4

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This paper cites , Ridgway , James J.

Variational Inference for Latent Variable Models in High Dimensions , Ridgway , James J

Reference 5

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This paper cites Convergence of coordinate ascent variational inference for log-concave measures via optimal transport.

Variational Inference for Latent Variable Models in High Dimensions Convergence of coordinate ascent variational inference for log-concave measures via optimal transport

Reference 6

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This paper cites ( 2021 ).

Variational Inference for Latent Variable Models in High Dimensions ( 2021 )

Reference 7

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This paper cites , Pati , Debdeep D.

Variational Inference for Latent Variable Models in High Dimensions , Pati , Debdeep D

Reference 8

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This paper cites , Choi , David D.

Variational Inference for Latent Variable Models in High Dimensions , Choi , David D

Reference 9

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Variational Inference for Latent Variable Models in High Dimensions , Bunea , Florentina F

Reference 10

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Variational Inference for Latent Variable Models in High Dimensions Unresolved cited work

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Reference 12

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This paper cites A variational Bayes approach to debiased inference for low-dimensional parameters in high-dimensional linear regression.

Variational Inference for Latent Variable Models in High Dimensions A variational Bayes approach to debiased inference for low-dimensional parameters in high-dimensional linear regression

Reference 15

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This paper cites Dembo , Amir A.

Variational Inference for Latent Variable Models in High Dimensions Dembo , Amir A

Reference 16

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Variational Inference for Latent Variable Models in High Dimensions , Handschy , Madeline M

Reference 17

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Variational Inference for Latent Variable Models in High Dimensions Rigon , Tommaso T

Reference 20

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Variational Inference for Latent Variable Models in High Dimensions ( 2018 )

Reference 21

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Variational Inference for Latent Variable Models in High Dimensions , Stephens , Matthew M

Reference 22

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This paper cites Gradient flows for empirical Bayes in high-dimensional linear models.

Variational Inference for Latent Variable Models in High Dimensions Gradient flows for empirical Bayes in high-dimensional linear models

Reference 23

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Variational Inference for Latent Variable Models in High Dimensions , Boyles , Levi L

Reference 24

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Variational Inference for Latent Variable Models in High Dimensions , Ma , Zongming Z

Reference 25

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Variational Inference for Latent Variable Models in High Dimensions Klopp , Olga O

Reference 26

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Variational Inference for Latent Variable Models in High Dimensions , Javadi , Hamid H

Reference 27

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Variational Inference for Latent Variable Models in High Dimensions Dunson , David B D

Reference 29

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Variational Inference for Latent Variable Models in High Dimensions ( 1982 )

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Variational Inference for Latent Variable Models in High Dimensions , Liu , Yan Y

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Variational Inference for Latent Variable Models in High Dimensions , Wang , Wei W

Reference 35

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Variational Inference for Latent Variable Models in High Dimensions Zanella , Giacomo G

Reference 37

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Variational Inference for Latent Variable Models in High Dimensions Rinaldo , Alessandro A

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Variational Inference for Latent Variable Models in High Dimensions , Moss , Fabian C F

Reference 39

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Variational Inference for Latent Variable Models in High Dimensions On Naive Mean-Field Approximation for high-dimensional canonical GLMs

Reference 40

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Variational Inference for Latent Variable Models in High Dimensions A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression

Reference 41

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Variational Inference for Latent Variable Models in High Dimensions Sen , Subhabrata S

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Variational Inference for Latent Variable Models in High Dimensions , Bhattacharya , Anirban A

Reference 44

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Variational Inference for Latent Variable Models in High Dimensions Szab \'o , Botond B

Reference 46

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Variational Inference for Latent Variable Models in High Dimensions , Chu , Jing J

Reference 49

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Variational Inference for Latent Variable Models in High Dimensions ( 2014 )

Reference 51

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Variational Inference for Latent Variable Models in High Dimensions Kumar , Sathish AP S

Reference 52

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This paper cites Blei , David M D.

Variational Inference for Latent Variable Models in High Dimensions Blei , David M D

Reference 54

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Variational Inference for Latent Variable Models in High Dimensions , Yang , Wen W

Reference 55

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Observation 412bdb3d-e732-43c1-9fbc-e193f1081915 · outbound

This paper cites Blei , David M D.

Variational Inference for Latent Variable Models in High Dimensions Blei , David M D

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Variational Inference for Latent Variable Models in High Dimensions Gao , Chao C

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Variational Bayes and Truncation approximations for Enriched Dirichlet process mixtures cites this paper.

Variational Bayes and Truncation approximations for Enriched Dirichlet process mixtures Variational Inference for Latent Variable Models in High Dimensions

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