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Paper Citation Record · LEDGER

Learning Juntas under Markov Random Fields

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2506.00764.

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

pith.paper-citation-record.v1
2506.00764 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:10:52.863406Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T19:45:54.008353Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T22:30:52.949291Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact2
  • verified fuzzy39
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c14fa93f-5bf7-4189-9bf4-419f89bacd09 · outbound

This paper cites Public-key cryptography from different assumptions.

Learning Juntas under Markov Random Fields Public-key cryptography from different assumptions

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:59.090743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:49.726296Z digest=sha256:0e4712e45c3b0f795a71ddd77f770ef2a677a29ecd94b49658fb040b511b5f05

Observation 3f811d25-b2b7-4d4f-a5a3-24fdfeb21c86 · outbound

This paper cites Learning factor graphs in polynomial time and sample complexity.

Learning Juntas under Markov Random Fields Learning factor graphs in polynomial time and sample complexity

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:58.855445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:49.806707Z digest=sha256:2fdec888781f37ace67471da29f0bf3f9043eaf12d48529a8d47f53d06e6979f

Observation c59d4cf7-edd3-45b0-825a-fa7ae1c80cac · outbound

This paper cites Agnostically learning juntas from random walks, 2008.

Learning Juntas under Markov Random Fields Agnostically learning juntas from random walks, 2008

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:58.612935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:49.943589Z digest=sha256:5843daecb8160a4fd27e062246089c601965be7f5012bc23d49ae8639a797b7c

Observation 0d77d2ca-949b-4b42-996a-6463a6d5c9c7 · outbound

This paper cites Id3 learns juntas for smoothed product distributions.

Learning Juntas under Markov Random Fields Id3 learns juntas for smoothed product distributions

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:58.418923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.026129Z digest=sha256:c153b6056534d290c8d6345d5636c0c71deca9a074bc59077e9534832e94164a

Observation 8d7b3158-36dd-434f-964e-5da45e7707b4 · outbound

This paper cites Weakly learning dnf and characterizing statistical query learning using fourier analysis.

Learning Juntas under Markov Random Fields Weakly learning dnf and characterizing statistical query learning using fourier analysis

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:58.230993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.121268Z digest=sha256:c8e1452b4d710820acd32d019a784b2a453a513dfb602d7f1020fa2b25b9ebfc

Observation 42fe576d-9328-457c-be62-72b8215e70ce · outbound

This paper cites Near-optimal learning of tree-structured distributions by chow-liu.

Learning Juntas under Markov Random Fields Near-optimal learning of tree-structured distributions by chow-liu

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:58.082206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 2b3c1f07-aba4-46d2-b1e8-b307da4c7274 · outbound

This paper cites Blum and Pat Langley.

Learning Juntas under Markov Random Fields Blum and Pat Langley

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:57.934299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.275521Z digest=sha256:cfa8b6093c274f0dc94ea044881d7498984588a6307a733bae4c7cabedde2ccb

Observation 402ae891-f2d6-4ccd-b183-60a6ce2467a7 · outbound

This paper cites Improved bounds for testing juntas.

Learning Juntas under Markov Random Fields Improved bounds for testing juntas

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:57.774626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.369859Z digest=sha256:4050b0730ce8434626f4ff3e7a71707de5c47c0297892859fa16cc54a86bb7e4

Observation bf3f7cbb-5d9a-4bae-b1cd-f0fc924c5a35 · outbound

This paper cites Testing juntas nearly optimally.

Learning Juntas under Markov Random Fields Testing juntas nearly optimally

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:57.581882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.413762Z digest=sha256:d0fdea41419d987606d659c86ad56bb2e46de0ef468d89f484bdaa98c5bb7502

Observation 2aaec35d-56cc-4d85-937c-1e783115c18a · outbound

This paper cites Relevant examples and relevant features: Thoughts from computational learning theory.

Learning Juntas under Markov Random Fields Relevant examples and relevant features: Thoughts from computational learning theory

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:57.401706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.454489Z digest=sha256:294cf04a94afca07a84231058146c87bc8b769bdb0922a2ab5d998eadd7caf9a

Observation 51825a21-c69f-4ce6-87aa-511765cd1491 · outbound

This paper cites Bshouty, E.

Learning Juntas under Markov Random Fields Bshouty, E

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:57.239577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.509187Z digest=sha256:9907a821dd2cfc10bdf7b11de73ec49332c74a07b060c3d19a4269427e6a0543

Observation 84c362e3-8b0b-4780-9a47-0aeee785dc53 · outbound

This paper cites Reconstruction of markov random fields from samples: Some observations and algorithms.

Learning Juntas under Markov Random Fields Reconstruction of markov random fields from samples: Some observations and algorithms

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:57.040463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.577177Z digest=sha256:a3c30c22869b9afd04b10b23010d57c8b1167fa48b01168d759217f072fe448f

Observation bc35fa2b-8c38-49ab-a33a-22091d2fd26f · outbound

This paper cites Efficiently learning ising models on arbitrary graphs.

Learning Juntas under Markov Random Fields Efficiently learning ising models on arbitrary graphs

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.834636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.663865Z digest=sha256:7b455919b858f841c355dbbc71c88975aa2a3e30607fdb103eebfb658e13b8b1

Observation 2ef59dcd-fb50-4567-a4ba-9e34c66b8ef5 · outbound

This paper cites Markov fields on finite graphs and lattices.

Learning Juntas under Markov Random Fields Markov fields on finite graphs and lattices

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.690799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.698020Z digest=sha256:d788fb21470012e5651df33b6089bb489bdccfe6e8ce00a252dbac2cf4b4d83f

Observation 71248075-528d-421e-81f6-fff3ab25f7c0 · outbound

This paper cites Learning the Sherrington-Kirkpatrick Model Even at Low Temperature.

Learning Juntas under Markov Random Fields Learning the Sherrington-Kirkpatrick Model Even at Low Temperature

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:10:53.185922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.753145Z digest=sha256:17f43ddabbf81f1f568eefaf0557d9105f1e0c8913a56643a22459735d1dbf69

Observation 4aa23442-262c-4782-83ac-f2922f1e787f · outbound

This paper cites Smoothed analysis for learning concepts with low intrinsic dimension.

Learning Juntas under Markov Random Fields Smoothed analysis for learning concepts with low intrinsic dimension

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.546315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.786241Z digest=sha256:044a283150b16e7e508b5185efdde17677f48972a2696b8bbb9b9aa1daffaade

Observation dbe93f9c-5596-4643-b622-de7ee1bf23de · outbound

This paper cites Approximating discrete probability distributions with dependence trees.

Learning Juntas under Markov Random Fields Approximating discrete probability distributions with dependence trees

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T12:10:50.805689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:10:50.805689Z digest=sha256:6068ea45925e0a51ead3b98642a0eead3e470c8468b90cd0193a7338b9948d09

Observation 87bacd2a-437f-4eb1-ac8a-397b26d6b4d6 · outbound

This paper cites Learning ising models from one or multiple samples.

Learning Juntas under Markov Random Fields Learning ising models from one or multiple samples

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.381990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.842500Z digest=sha256:a37a144350003e09ec302d5eb1525fbc6ad82bb1284c06703d74bdfee985a6f7

Observation 459ab760-f150-485e-b93c-04961f851fa4 · outbound

This paper cites Outlier-robust learning of ising models under dobrushin’s condition.

Learning Juntas under Markov Random Fields Outlier-robust learning of ising models under dobrushin’s condition

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.317260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:50.986211Z digest=sha256:c5995d748f3a9ae0c81d126bccc61b4d9b005524a8279ff1ed378ea0147f6a07

Observation d7a288c9-389f-4e34-897e-669b2a1d8745 · outbound

This paper cites Testing juntas.

Learning Juntas under Markov Random Fields Testing juntas

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.243188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.055243Z digest=sha256:c11955ed95daf7f5dac48e49a3c449a2f730472652fa6067494319d846a14da2

Observation a4dd99df-508e-4e07-8d32-d8cbebfb4ea5 · outbound

This paper cites Learning ising models with independent failures.

Learning Juntas under Markov Random Fields Learning ising models with independent failures

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.181511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.119692Z digest=sha256:e95f030afa7daca074e7bd53683ebab0329e9de81bf17c49d08803e25a0241ce

Observation 4bbd9d23-0723-4d62-9bed-0e508dd0d6c3 · outbound

This paper cites Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics.

Learning Juntas under Markov Random Fields Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:10:53.043047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.207874Z digest=sha256:7bf92f9bc11e2e29a58a88a7f25b9bba53408fc300cd7aeb199f52c5e32e4e4f

Observation 5e99d183-190a-4c54-9d44-61a15217b316 · outbound

This paper cites Information theoretic properties of markov random fields, and their algorithmic applications.

Learning Juntas under Markov Random Fields Information theoretic properties of markov random fields, and their algorithmic applications

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:56.066577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b8986740-b813-42ef-b23b-0183dcf9b347 · outbound

This paper cites Smoothed analysis of online and differentially private learning.

Learning Juntas under Markov Random Fields Smoothed analysis of online and differentially private learning

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:55.887797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.376468Z digest=sha256:5afbd8194b7a92f2735a2387dce02197aa72c0efcff3840ec66ae2667ba9715d

Observation 8cc57d43-a9e5-4c0f-9473-c596b0f773b3 · outbound

This paper cites Smoothed analysis with adaptive adversaries.

Learning Juntas under Markov Random Fields Smoothed analysis with adaptive adversaries

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:55.707768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.462390Z digest=sha256:0e584b5384def5e3da672d2931200b2f46d860cbb3342e32e8b4aca53193c786

Observation 31ce9f80-1bdb-4949-883e-9980d88bd8f2 · outbound

This paper cites Jackson and Karl Wimmer.

Learning Juntas under Markov Random Fields Jackson and Karl Wimmer

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:55.573067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.532002Z digest=sha256:c71354a754f466450c01cd671c7b3b9cd18636394302db77a655406997d2650d

Observation 764d0311-6ef8-4a64-bb99-176cc0bc1112 · outbound

This paper cites Efficient noise-tolerant learning from statistical queries.

Learning Juntas under Markov Random Fields Efficient noise-tolerant learning from statistical queries

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:55.420226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.609804Z digest=sha256:6c8cf19d70172a6e5eea7322f88a3e7316405b17c1e31e407877f366127ab96e

Observation 5142bc12-532b-443d-a911-cbc80a239efd · outbound

This paper cites Mcmc learning.

Learning Juntas under Markov Random Fields Mcmc learning

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:55.310470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.674141Z digest=sha256:2a5d4199f18016637309dc26258444b93ffa30caabd420d8c7c4f79c9125e7f4

Observation a355d7fb-ca25-4376-95e5-f08697360eb3 · outbound

This paper cites Klivans and Raghu Meka.

Learning Juntas under Markov Random Fields Klivans and Raghu Meka

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:55.185388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.760249Z digest=sha256:991b1c7ca0b36bca92ca6d59f3eaa97208c19b0f9459b01f2e9ad51484599c0d

Observation 90c92a97-f0e3-4c44-9576-fad811fb5c1d · outbound

This paper cites Learning and smoothed analysis.

Learning Juntas under Markov Random Fields Learning and smoothed analysis

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:55.076746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:51.843278Z digest=sha256:020debb93e5ac15960f9a0b807e5709261e1c12fbd0dd9754fa7adaa9d0aa0af

Observation e21e68c0-d3d4-42cd-8b98-68abbf8da2d7 · outbound

This paper cites Decision trees are PAC-learnable from most product distributions: a smoothed analysis.

Learning Juntas under Markov Random Fields Decision trees are PAC-learnable from most product distributions: a smoothed analysis

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T12:10:51.930523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:10:51.930523Z digest=sha256:1f9e4155363bc8156a3ce001cd15b0f13aa24ef5bdc6c8a9036510bcab81c328

Observation f7348139-3e71-49ae-8b08-b4bd0ecb57ae · outbound

This paper cites Learning to sample from censored markov random fields.

Learning Juntas under Markov Random Fields Learning to sample from censored markov random fields

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:54.945818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:52.024671Z digest=sha256:a5e5cc59b4a7d1babef02bb04057d11157678e74b40084e68211733a7e6c92f1

Observation 5d9baaa1-6737-4887-98a3-9d05693a67d9 · outbound

This paper cites Servedio.

Learning Juntas under Markov Random Fields Servedio

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:54.824592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:52.136481Z digest=sha256:d9de36a732f0aa33ef4cf40f44db9f29d98c7f56b6da5ef0d160f914b41fe766

Observation 67e283b1-4ed3-4d95-8585-a4ef4e50d234 · outbound

This paper cites Greedy learning of markov network structure.

Learning Juntas under Markov Random Fields Greedy learning of markov network structure

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:54.672438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:52.235484Z digest=sha256:51acd4ed8cc1d44589473019eec2cd8560e4690c1343cbd28b5b4f8c6ab82946

Observation 545bdbbc-4e1d-4763-9764-1076b85d8f1e · outbound

This paper cites Proclaiming dictators and juntas or testing boolean formulae.

Learning Juntas under Markov Random Fields Proclaiming dictators and juntas or testing boolean formulae

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:54.529929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:10:52.312663Z digest=sha256:d798dbd660c64c96b95c7456fc7e52f5bd6b8e2fd66aefb9fe0996ad4875d7f3

Observation 2bdf4c17-315d-4f9f-807f-fb41e9edc409 · outbound

This paper cites On learning ising models under huber's contamination model.

Learning Juntas under Markov Random Fields On learning ising models under huber's contamination model

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:54.405494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 8a534746-17fe-4f52-b777-08ed496da169 · outbound

This paper cites Spielman and Shang-Hua Teng.

Learning Juntas under Markov Random Fields Spielman and Shang-Hua Teng

Reference 37

Resolution
verified fuzzy
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source=arxiv_source observed=2026-08-07T12:10:52.450664Z digest=sha256:1ff900f217c00d82366fe9524e5809170dda2b2389ca49c38319b5a9d7261970

Observation 0b78679e-6d62-4301-88fa-9f1e5ae830ce · outbound

This paper cites Information-theoretic limits of selecting binary graphical models in high dimensions.

Learning Juntas under Markov Random Fields Information-theoretic limits of selecting binary graphical models in high dimensions

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:54.156615Z

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source=arxiv_source observed=2026-08-07T12:10:52.527746Z digest=sha256:312abdc4888f24f6659d17ea460a96ed8b9d0323419b871ba608b8213ea45e00

Observation 59b14a0a-f419-4077-b703-1ef40043e1f9 · outbound

This paper cites Learning graphs with a few hubs.

Learning Juntas under Markov Random Fields Learning graphs with a few hubs

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:53.993524Z

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source=arxiv_source observed=2026-08-07T12:10:52.594425Z digest=sha256:01e8563d5c0d9acbe3b89ef6de17159393a7e8c7ec84bb01a7d72e7065508439

Observation a3a284ab-b75b-4080-861a-65df306635b9 · outbound

This paper cites Finding correlations in subquadratic time, with applications to learning parities and juntas.

Learning Juntas under Markov Random Fields Finding correlations in subquadratic time, with applications to learning parities and juntas

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:53.857477Z

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source=arxiv_source observed=2026-08-07T12:10:52.667891Z digest=sha256:c28229ed391313088c4e89f31a75481d4dc935fa6b00c8e93c55f9005453d33f

Observation a2f62ba8-17be-42f3-9de1-d9cff061e311 · outbound

This paper cites Lokhov, and Michael Chertkov.

Learning Juntas under Markov Random Fields Lokhov, and Michael Chertkov

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:53.686490Z

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source=arxiv_source observed=2026-08-07T12:10:52.700667Z digest=sha256:5030a68ab2ad29c0e00dd245b1b4188976747dd0a8d3fbe77407759aaa7a4dcf

Observation 4096cb9a-f237-4fb4-b2be-e96e85fdcdbd · outbound

This paper cites High-dimensional graphical model selection using _1 -regularized logistic regression.

Learning Juntas under Markov Random Fields High-dimensional graphical model selection using _1 -regularized logistic regression

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:53.474812Z

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source=arxiv_source observed=2026-08-07T12:10:52.786413Z digest=sha256:a7a08184c86cd344b102521a05ec2da54bf46f9149e23d8c49524cef4addb5aa

Observation 60159897-dc12-4051-89d5-0593a5fb1bad · outbound

This paper cites Sparse logistic regression learns all discrete pairwise graphical models.

Learning Juntas under Markov Random Fields Sparse logistic regression learns all discrete pairwise graphical models

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:10:53.338131Z

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source=arxiv_source observed=2026-08-07T12:10:52.863406Z digest=sha256:08d5800eb6fc5f094d8fbc8196668ed2d9e6847af2d6a83f00effb13d137ba93

Pith citing papers

Observation ce7cc849-353f-489f-b93e-f9b2dd599579 · inbound

Learning $\mathsf{AC}^0$ Under Graphical Models cites this paper.

Learning $\mathsf{AC}^0$ Under Graphical Models Learning Juntas under Markov Random Fields

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:30:52.955246Z

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