Paper Citation Record · LEDGER

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 3 inbound Pith citation observations for arXiv:2506.11647.

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

pith.paper-citation-record.v1
2506.11647 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:15:01.553238Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:25:34.934769Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:16:35.038381Z

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy35
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8aba1cd0-ac79-4581-9524-e00cfef9b15f · outbound

This paper cites Distributed subgradient methods for multi-agent optimiza- tion,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Distributed subgradient methods for multi-agent optimiza- tion,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.240619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7b3426f4-ff69-4314-8077-ae9eb8033be4 · outbound

This paper cites A new approach to consensus problems in discrete-time multiagent 18 systems with time-delays,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise A new approach to consensus problems in discrete-time multiagent 18 systems with time-delays,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.226564Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fab57a5-4da6-4e84-805e-32893016db90 · outbound

This paper cites Controllability of multi-agent systems based on agreement protocols,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Controllability of multi-agent systems based on agreement protocols,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.212097Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 54cd9b08-7e43-4e42-a945-904494a1ee80 · outbound

This paper cites Finite-time consensus problems for networks of dynamic agents,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Finite-time consensus problems for networks of dynamic agents,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.198388Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f58c1d9b-e155-4f82-99f6-ba9f67b16a3a · outbound

This paper cites Distributed optimization over time-varying directed graphs,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Distributed optimization over time-varying directed graphs,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.184194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8b83c1b-97d6-4fea-8ec4-b688c0f05832 · outbound

This paper cites Distributed continuous-time convex optimization with time- varying cost functions,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Distributed continuous-time convex optimization with time- varying cost functions,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.170125Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 281b1a73-9bce-44c8-9185-ff97b9df80ed · outbound

This paper cites Large-scale distributed dedicated- and non-dedicated smart city sensing systems,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Large-scale distributed dedicated- and non-dedicated smart city sensing systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.155339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f98879c5-7c0b-40c1-ba51-be67872cb793 · outbound

This paper cites Zhu and S.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Zhu and S

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.140926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9b9949e0-0b85-466e-bff9-8b221e6a85c8 · outbound

This paper cites Initialization-free distributed fixed-time convergent algorithms for optimal resource allocation,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Initialization-free distributed fixed-time convergent algorithms for optimal resource allocation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.126538Z

Source-reported events for the cited work

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Observation d21338b8-26f4-4f2b-bc9d-f670c8016f54 · outbound

This paper cites Wireless sensor networks for environmental monitoring: The sensorscope experience,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Wireless sensor networks for environmental monitoring: The sensorscope experience,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.112349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e01df233-a14f-448c-9eae-786e429641eb · outbound

This paper cites Network topology and communication- computation tradeoffs in decentralized optimization,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Network topology and communication- computation tradeoffs in decentralized optimization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.096572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation daec4f9a-a64c-43ee-bfdd-018cd266d177 · outbound

This paper cites A general framework for decentralized opti- mization with first-order methods,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise A general framework for decentralized opti- mization with first-order methods,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.080616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 607712fa-1541-4f41-b9d8-d7a44ba49e9e · outbound

This paper cites Asymptotic network independence in dis- tributed stochastic optimization for machine learning: Examining distributed and central- ized stochastic gradient descent,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Asymptotic network independence in dis- tributed stochastic optimization for machine learning: Examining distributed and central- ized stochastic gradient descent,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.066102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 534b99da-4305-493d-9f45-58dab91f8828 · outbound

This paper cites Gradient-tracking-based distributed optimization with guaranteed optimality under noisy information sharing,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Gradient-tracking-based distributed optimization with guaranteed optimality under noisy information sharing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.050619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68f37e39-36e2-476e-ab7c-4391ed233083 · outbound

This paper cites Event-triggered distributed stochastic mirror descent for convex optimization,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Event-triggered distributed stochastic mirror descent for convex optimization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.035883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eb8abdca-5ef5-4537-ac04-6be312cd4bfa · outbound

This paper cites High-probability convergence bounds for non- convex stochastic gradient descent,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise High-probability convergence bounds for non- convex stochastic gradient descent,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:01.439171Z

Source-reported events for the cited work

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Observation 31fb552f-07d7-416f-a8d0-c557f8c30f16 · outbound

This paper cites High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:01.443520Z

Source-reported events for the cited work

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Observation 9cdb571f-8c90-41c7-a2f5-c92c2889c97e · outbound

This paper cites High probability conver- gence of stochastic gradient methods,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise High probability conver- gence of stochastic gradient methods,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.021398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 86e69ce8-f407-485d-b263-da99670a7a33 · outbound

This paper cites Convergence in high probability of distributed stochastic gradient descent algorithms,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Convergence in high probability of distributed stochastic gradient descent algorithms,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:02.006350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a197036-a4d7-405f-9a13-82a85befc603 · outbound

This paper cites Distributed (atc) gradient descent for high dimension sparse regression,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Distributed (atc) gradient descent for high dimension sparse regression,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.992290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c5d5e75e-c158-4201-8f7a-9a9ea12d1ab8 · outbound

This paper cites L´evy flights in evolutionary ecology,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise L´evy flights in evolutionary ecology,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.976584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 34e34e32-21c7-4e16-99ec-cf3f9cfdb46d · outbound

This paper cites Generalized wiener filtering with fractional power spectro- grams,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Generalized wiener filtering with fractional power spectro- grams,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.961623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2700a2f7-077e-4501-8a43-a3dfddd2ccb6 · outbound

This paper cites Fractals and scaling in finance,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Fractals and scaling in finance,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.947024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7d5167ee-4247-46f4-87d1-7e10bac7790d · outbound

This paper cites A tail-index analysis of stochastic gradi- ent noise in deep neural networks,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise A tail-index analysis of stochastic gradi- ent noise in deep neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.931583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation af07adbb-924b-4393-91f5-7b7a1aa8c4b9 · outbound

This paper cites On the difficulty of training recurrent neural networks,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise On the difficulty of training recurrent neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.916789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f66f735e-1d8e-4db2-ba97-ce691638a65b · outbound

This paper cites Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.901086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4008a803-ac36-480d-a53f-0d07eb6857fd · outbound

This paper cites An ac- celerated method for decentralized distributed stochastic optimization over time-varying graphs,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise An ac- celerated method for decentralized distributed stochastic optimization over time-varying graphs,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.886051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0bb79662-e9f8-4eed-8c6e-e79c9323d158 · outbound

This paper cites High Probability Convergence of Clipped-SGD Under Heavy-tailed Noise.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise High Probability Convergence of Clipped-SGD Under Heavy-tailed Noise

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:01.497885Z

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Observation 83adfd2a-3a63-442b-b3d3-7870a9b9298f · outbound

This paper cites High-probability bounds for stochastic optimization and varia- tional inequalities: the case of unbounded variance,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise High-probability bounds for stochastic optimization and varia- tional inequalities: the case of unbounded variance,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.870002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 05813027-46ac-4298-8bc5-f13d42b3f0db · outbound

This paper cites High-probability convergence for composite and distributed stochastic minimization and variational inequalities with heavy-tailed noise,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise High-probability convergence for composite and distributed stochastic minimization and variational inequalities with heavy-tailed noise,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.854585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ee049291-4f25-4222-904d-cc4431e18a9e · outbound

This paper cites Distributed online optimization in dynamic environ- ments using mirror descent,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Distributed online optimization in dynamic environ- ments using mirror descent,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.839213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation caa17b06-7462-47d7-9c44-36eb67061e40 · outbound

This paper cites Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tails.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tails

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:01.516951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0075f76-2954-4b3d-a63b-11b23da3408b · outbound

This paper cites Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:15:01.598132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b506689-ef80-461e-bba6-61e142221d53 · outbound

This paper cites Problem complexity and method efficiency in opti- mization,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Problem complexity and method efficiency in opti- mization,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.824149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e7fa612-f723-4248-be88-419798c806dc · outbound

This paper cites Why are adaptive methods good for attention models?.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Why are adaptive methods good for attention models?

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.807692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6867699b-0b72-4496-bf6f-52edfed74bf4 · outbound

This paper cites Asynchronous consensus in continuous-time multi-agent systems with switching topology and time-varying delays,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Asynchronous consensus in continuous-time multi-agent systems with switching topology and time-varying delays,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.790886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f73c731-c6f5-4662-a302-4aeb6434d9de · outbound

This paper cites Group consensus in multi-agent systems with switching topologies,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Group consensus in multi-agent systems with switching topologies,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.774753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b60c584a-1273-431d-beb1-ac3e88f364e7 · outbound

This paper cites A new class of distributed optimiza- tion algorithms: Application to regression of distributed data,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise A new class of distributed optimiza- tion algorithms: Application to regression of distributed data,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.759209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1322ebc0-9463-444a-9302-f947a6546b42 · outbound

This paper cites On the difficulty of training recurrent neural networks,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise On the difficulty of training recurrent neural networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.743334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 857fc0c8-a18c-4b82-87c0-531ddc8e534c · outbound

This paper cites Stochastic optimization with heavy-tailed noise via accelerated gradient clipping,.

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise Stochastic optimization with heavy-tailed noise via accelerated gradient clipping,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:01.728039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 94739c9a-cb15-4adf-8f40-bac5985b5e61 · inbound

DeMuon: A Decentralized Muon for Matrix Optimization over Graphs cites this paper.

DeMuon: A Decentralized Muon for Matrix Optimization over Graphs High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T13:25:34.934769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41d4b4f2-ca32-42d1-aed8-385b0a5510d4 · inbound

High-Probability Convergence Guarantees of Decentralized SGD cites this paper.

High-Probability Convergence Guarantees of Decentralized SGD High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:46:07.865839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 88d49083-6c25-41bb-ac29-ea5ab0b97e6f · inbound

High-Probability Convergence Guarantees of Decentralized SGD cites this paper.

High-Probability Convergence Guarantees of Decentralized SGD High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:16:35.042217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.