Paper Citation Record · LEDGER

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning

As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.06693.

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

pith.paper-citation-record.v1
1908.06693 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:47:24.860546Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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 exact1
verified fuzzy28
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dec812ee-b7a2-43ca-b7bf-fcd62f91cb11 · outbound

This paper cites Scaling distributed ma chine learning with the parameter server,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Scaling distributed ma chine learning with the parameter server,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.515622Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 63dd8017-4892-4e6e-9f23-c2b48faadd4d · outbound

This paper cites A comparison of distributed machine learning platforms,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning A comparison of distributed machine learning platforms,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.493230Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 839db03d-f7b5-47cb-9056-82b9377685e7 · outbound

This paper cites An adaptive synchronous parallel strategy for distributed ma chine learning,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning An adaptive synchronous parallel strategy for distributed ma chine learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.467926Z

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Observation df53c4ab-afc3-447f-938e-e823878066b0 · outbound

This paper cites Communicati on efﬁcient distributed machine learning with the parameter s erver,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Communicati on efﬁcient distributed machine learning with the parameter s erver,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.449062Z

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Observation 286d8a8d-0780-4265-bab8-b96e1ba8e99f · outbound

This paper cites Federated learning: Strategies for improving co mmunication efﬁciency,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Federated learning: Strategies for improving co mmunication efﬁciency,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.424953Z

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Observation f2961df8-4653-42d2-b85c-b21100caf28f · outbound

This paper cites Communication-efﬁcient learning of deep networks from de centralized data,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Communication-efﬁcient learning of deep networks from de centralized data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.406249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b17307a0-3a7c-49cd-a43e-3048a875b249 · outbound

This paper cites Optimization meth ods for large- scale machine learning,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Optimization meth ods for large- scale machine learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.387933Z

Source-reported events for the cited work

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Observation 23b9f998-fff6-4c91-9a82-8a3440538600 · outbound

This paper cites An approximate dual subgradient a lgorithm for multi-agent non-convex optimization,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning An approximate dual subgradient a lgorithm for multi-agent non-convex optimization,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.367160Z

Source-reported events for the cited work

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Observation 6b5d95c7-6bcd-4d13-b554-33f07893cc52 · outbound

This paper cites Nestt: A non convex primal-dual splitting method for distributed and stochast ic optimization,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Nestt: A non convex primal-dual splitting method for distributed and stochast ic optimization,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.347747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 94a1d146-39b3-46d4-b9c8-24529bd802d2 · outbound

This paper cites Prox-PDA: The p roximal primal-dual algorithm for fast distributed nonconvex opti mization and learning over networks,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Prox-PDA: The p roximal primal-dual algorithm for fast distributed nonconvex opti mization and learning over networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.325946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f5be19c1-d509-4b28-b513-d612ab77b6dc · outbound

This paper cites On the converg ence of a distributed augmented lagrangian method for nonconvex opt imization,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning On the converg ence of a distributed augmented lagrangian method for nonconvex opt imization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.303689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6cfbdfab-9e7c-41a7-81cb-0883d133c027 · outbound

This paper cites Paralle l and distributed methods for constrained nonconvex optimizationpart i: The ory,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Paralle l and distributed methods for constrained nonconvex optimizationpart i: The ory,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.280038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c5f40666-7490-4f19-ba6d-10b30f440e43 · outbound

This paper cites NEXT: In-network nonconv ex optimiza- tion,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning NEXT: In-network nonconv ex optimiza- tion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.259453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ff85442f-42a4-4efb-b185-f81e16cdb1a9 · outbound

This paper cites A distributed, asynchronous, and incrementa l algorithm for nonconvex optimization: An ADMM approach,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning A distributed, asynchronous, and incrementa l algorithm for nonconvex optimization: An ADMM approach,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.236467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d7c0fd39-84f5-4a19-b86c-b36d8310dea7 · outbound

This paper cites A case for nonconvex dis tributed optimization in large-scale power systems,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning A case for nonconvex dis tributed optimization in large-scale power systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.217021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3444dcbb-d2aa-407d-a084-ed74254b68d9 · outbound

This paper cites Convergence analys is of alternating direction method of multipliers for a family of nonconvex problems,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Convergence analys is of alternating direction method of multipliers for a family of nonconvex problems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.199931Z

Source-reported events for the cited work

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Observation fcd50acb-e073-424f-84cf-480fdeb754d4 · outbound

This paper cites On nonconvex decentralized gradien t descent,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning On nonconvex decentralized gradien t descent,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.183247Z

Source-reported events for the cited work

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Observation e157d28d-08ca-4303-ae3b-62eebde358ee · outbound

This paper cites Zone: Zeroth ord er nonconvex multi-agent optimization over networks,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Zone: Zeroth ord er nonconvex multi-agent optimization over networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.166081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eacd49cb-d441-4dd9-b186-a731a914928c · outbound

This paper cites Stochastic gradient-push for strongly convex functions on time-varying directed graphs,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Stochastic gradient-push for strongly convex functions on time-varying directed graphs,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.149086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cce131a5-5fbb-48d3-af4b-4037139112ad · outbound

This paper cites Conver gence rates for distributed stochastic optimization over random netwo rks,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Conver gence rates for distributed stochastic optimization over random netwo rks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.131811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 322cef45-0a1d-40ea-ac1f-db37f8c0efae · outbound

This paper cites SUCAG : Stochastic unbiased curvature-aided gradient method for distributed optimization,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning SUCAG : Stochastic unbiased curvature-aided gradient method for distributed optimization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.115744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 418054d7-1035-473e-a2be-dd02c2114b94 · outbound

This paper cites Distributed Stochastic Gradient Tracking Methods.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Distributed Stochastic Gradient Tracking Methods

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5ae23765-81ea-4e76-a125-cf5523de118e · outbound

This paper cites Non-convex distributed opt imization,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Non-convex distributed opt imization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.099759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 08aeebb8-60bb-4901-9970-fcdd7468d126 · outbound

This paper cites Convergence of a multi-a gent projected stochastic gradient algorithm for non-convex optimizatio n,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Convergence of a multi-a gent projected stochastic gradient algorithm for non-convex optimizatio n,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.083171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 24b9b23b-f77e-4206-8dd4-735c8732bb51 · outbound

This paper cites Khalil, Nonlinear Systems.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Khalil, Nonlinear Systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.062622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4eb2f1f0-4f99-4748-8e6a-a8abb2f52070 · outbound

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Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Unresolved cited work

Reference 26

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Source-reported events for the cited work

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Observation a2406bf3-7a44-4a00-9c93-fd1374cd4af8 · outbound

This paper cites Distributed subgradient me thods for multi- agent optimization,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Distributed subgradient me thods for multi- agent optimization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.030322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 27484ef9-3f47-4fb7-8b89-b2a93a6833dc · outbound

This paper cites First-order Methods Almost Always Avoid Saddle Points.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning First-order Methods Almost Always Avoid Saddle Points

Reference 28

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Source-reported events for the cited work

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Observation 49d102a4-b65b-4379-a2b8-83c6bdf50fcc · outbound

This paper cites Sharp Analysis for Nonconvex SGD Escaping from Saddle Points.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Sharp Analysis for Nonconvex SGD Escaping from Saddle Points

Reference 29

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Source-reported events for the cited work

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Observation 0ba4b784-de7f-4123-84e1-5d03894fab8a · outbound

This paper cites On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T12:47:24.843115Z

Source-reported events for the cited work

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Observation 988aaa21-bc86-4ad0-8702-e02bf55da4d0 · outbound

This paper cites Bishop, Pattern Recognition and Machine Learning , ser.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Bishop, Pattern Recognition and Machine Learning , ser

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:25.012433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa592bd4-cdbd-415c-9542-72fd14353dcc · outbound

This paper cites Distributed linear param eter estimation: Asymptotically efﬁcient adaptive strategies,.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning Distributed linear param eter estimation: Asymptotically efﬁcient adaptive strategies,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:24.995543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 43ca3445-83fc-422d-b98b-63e805c4258d · outbound

This paper cites A convergence theorem for n on negative almost supermartingales and some applications.

Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning A convergence theorem for n on negative almost supermartingales and some applications

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:47:24.978947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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